Projects
TimeIn: Traffic behaviour of interactive time-dependent thin streams on the modern Internet

The Internet as we know it today has been optimised to transmit large amounts of data or “greedy streams” - the type of transmission involved in downloading large files or watching online TV. Up to recently, Internet research has primarily focused on speeding up transmission by increasing bandwidth so that more data can be transferred at a given time. The most common Internet protocol for transmitting data, TCP, works by apportioning available bandwidth among the users present at any given time. The downside is that this can cause latency, or delay, in data transmissions. For time-dependant applications such as Internet telephony and online gaming, time lags as short as a few hundred milliseconds can create big problems.
In real-time gaming against other players online, data is transmitted only when an action such as moving around or shooting at someone is performed. The same principle applies for stock market programs when placing orders or requesting share prices, for example, via the trading systems in use the Norwegian Stock Exchange. In such cases it is essential to avoid any delay.
Applications like these often generate what are called thin data streams. With thin streams only small amounts of data are transmitted at a time and there can be extended periods between data packages. Such thin streams cannot compete with greedy traffic for bandwidth. Thin streams almost invariably come up short against greedy traffic and users are left to cope with the resulting lag. We want a more balanced Internet where thin streams don’t always lose out. This can be achieved by adding speed to the mix, instead of only thinking about maximising throughput.
Final goal:
Locate the sources of increased delay and loss rates for thin streams along the entire path of a data packet. Develop mechanisms that reduce the latency for time-dependent thin-stream applications without having to change current Internet infrastructure.
Funding source:
The Research Council of Norway
All partners:
- Coop. Assoc. for Internet Data Analysis (CAIDA)
- FUNCOM OSLO AS
- Universitetet i Oslo
- Karlstads Universitet
- UNINETT AS
- University of Kaiserslautern
- CISCO SYSTEMS NORWAY AS
RITE: Reducing Internet Transport Latency

RITE proposes to remove the root causes of unnecessary latency over the Internet. Whilst time-of-flight delay is inevitable, greater delays can result from interactions between transport protocols and buffers. It is this that RITE are tackling.
In RITE we aim to explore and develop changes to the existing infrastructure, so that the changes can be deployed without having to redesign the Internet. Latency can arise from a myriad of different reasons. As an example, setting up all connections for loading a dynamic web page, including DNS and background database connections, may delay the loading of the page with several extra RTTs. This will, unnecessarily, reduce the load speed. In RITE we analyse network traces and investigate network components and operating systems to locate the bottlenecks. RITE researchers use what we learn from this investigation to develop mechanisms that can be deployed in the existing infrastructure to improve the latency for generic Internet use, as well as for our chosen use-cases.
RITE has a strong focus on standardisation, and works through the IETF to transform the results of the project into standards. The partners also aim to contribute new code to the Linux kernel, making the improvements available to the public.
The project is driven by three specific use-cases: Financial applications, networked games and interactive video. These are applications with very different characteristics, but all have strict latency requirements. The industry partners will benefit from reduced Internet latency in a wide range of applications that they either provide infrastructure or hardware for, ultimately benefitting their customers.
Reducing latency may pave the way for new, exiting uses of the Internet. Applications that to this point have been unthinkable may be realised with consistent low-latency service. Also, lower latency for our time-dependent use-cases will improve the experience for the users drastically, giving our industry partners a competitive benefit.
Final goal:
The ultimate goal of RITE is to bring stable, low-latency services to Internet users and businesses, and lay the groundwork for consistent low latency in Internet communication.
Starting by better understanding how latency is traded off for throughput and what generates latency in the Internet, we will investigate the Internet systems to find the latency-inducing bottlenecks, both in the end-systems and in the network. Finally we will design mechanisms that can be integrated with the existing infrastructure to improve the experienced latency.
Funding source:
- The European Commission
- Funding scheme: STREP Total Cost: € 4,910,044.00 EC Contribution: € 3,569,000.00 Contract Number: CNECT-ICT-317700
All partners:
- Simula Research Laboratory (NO)
- British Telecommunications (UK)
- Alcatel-Lucent Bell (BE)
- Megapop Games (NO)
- University of Oslo (NO)
- Karlstad University (SE)
- Institut Mines-Telecom (FR)
- The University Court of the University of Aberdeen (UK)
Publications for RITE: Reducing Internet Transport Latency
Technical reports
More Accurate ECN Feedback in TCP
Internet Engineering Task Force, 2016.Status: Accepted
More Accurate ECN Feedback in TCP
Explicit Congestion Notification (ECN) is a mechanism where network nodes can mark IP packets instead of dropping them to indicate incipient congestion to the end-points. Receivers with an ECN-capable transport protocol feed back this information to the sender. ECN is specified for TCP in such a way that only one feedback signal can be transmitted per Round-Trip Time (RTT). Recently, new TCP mechanisms like Congestion Exposure (ConEx) or Data Center TCP (DCTCP) need more accurate ECN feedback information whenever more than one marking is received in one RTT. This document specifies an experimental scheme to provide more than one feedback signal per RTT in the TCP header. Given TCP header space is scarce, it overloads the three existing ECN-related flags in the TCP header and provides additional information in a new TCP option.
Afilliation | Communication Systems |
Project(s) | RITE: Reducing Internet Transport Latency, TimeIn: Traffic behaviour of interactive time-dependent thin streams on the modern Internet |
Publication Type | Technical reports |
Year of Publication | 2016 |
Number | draft-ietf-tcpm-accurate-ecn-02 |
Date Published | 10/2016 |
Publisher | Internet Engineering Task Force |
Keywords | Architecture, congestion control, Data Communication, Internet, networks, Protocols, QoS, Quality of Service, Rate Control, Security, Signalling, Standards |
Notes | (Work in Progress) |
URL | http://tools.ietf.org/html/draft-ietf-tcpm-accurate-ecn |
AQM Characterization Guidelines
IETF, 2016.Status: Published
AQM Characterization Guidelines
Unmanaged large buffers in today's networks have given rise to a slew of performance issues. These performance issues can be addressed by some form of Active Queue Management (AQM) mechanism, optionally in combination with a packet scheduling scheme such as fair queuing. The IETF Active Queue Management and Packet Scheduling working group was formed to standardize AQM schemes that are robust, easily implementable, and successfully deployable in today's networks. This document describes various criteria for performing precautionary characterizations of AQM proposals. This document also helps in ascertaining whether any given AQM proposal should be taken up for standardization by the AQM WG.
Afilliation | Communication Systems, Communication Systems, Communication Systems |
Project(s) | RITE: Reducing Internet Transport Latency, The Center for Resilient Networks and Applications |
Publication Type | Technical reports |
Year of Publication | 2016 |
Date Published | 07/2016 |
Publisher | IETF |
ISSN Number | 2070-1721 |
Keywords | Active Queue Management |
URL | https://tools.ietf.org/html/rfc7928 |
DualQ Coupled AQM for Low Latency, Low Loss and Scalable Throughput
Internet Engineering Task Force, 2016.Status: Submitted
DualQ Coupled AQM for Low Latency, Low Loss and Scalable Throughput
Data Centre TCP (DCTCP) was designed to provide predictably low queuing latency, near-zero loss, and throughput scalability using explicit congestion notification (ECN) and an extremely simple marking behaviour on switches. However, DCTCP does not co-exist with existing TCP traffic---throughput starves. So, until now, DCTCP could only be deployed where a clean-slate environment could be arranged, such as in private data centres. This specification defines `DualQ Coupled Active Queue Management (AQM)' to allow scalable congestion controls like DCTCP to safely co-exist with classic Internet traffic. The Coupled AQM ensures that a flow runs at about the same rate whether it uses DCTCP or TCP Reno/Cubic, but without inspecting transport layer flow identifiers. When tested in a residential broadband setting, DCTCP achieved sub-millisecond average queuing delay and zero congestion loss under a wide range of mixes of DCTCP and `Classic' broadband Internet traffic, without compromising the performance of the Classic traffic. The solution also reduces network complexity and eliminates network configuration.
Afilliation | Communication Systems |
Project(s) | RITE: Reducing Internet Transport Latency, TimeIn: Traffic behaviour of interactive time-dependent thin streams on the modern Internet |
Publication Type | Technical reports |
Year of Publication | 2016 |
Number | draft-briscoe-tsvwg-aqm-dualq-coupled-00 |
Date Published | 10/2016 |
Publisher | Internet Engineering Task Force |
Keywords | Algorithms, Analysis, AQM, Congestion Avoidance, congestion control, Data Communication, Design, Evaluation, Internet, latency, networks, Performance, QoS, Scaling, tcp |
Notes | (Work in Progress) |
URL | http://tools.ietf.org/html/draft-briscoe-tsvwg-aqm-dualq-coupled |
Journal Article
Reducing Internet Latency: A Survey of Techniques and their Merits
IEEE Communications Surveys and Tutorials 18, no. 3 (2014): 2149-2196.Status: Published
Reducing Internet Latency: A Survey of Techniques and their Merits
Latency is increasingly becoming a performance bottleneck for Internet Protocol (IP) networks, but historically networks have been designed with aims of maximizing throughput and utilization. This article offers a broad survey of techniques aimed at tackling latency in the literature up to August 2014, and their merits. A goal of this work is to be able to quantify and compare the merits of the different Internet latency reducing techniques, contrasting their gains in delay reduction versus the pain required to implement and deploy them. We found that classifying techniques according to the sources of delay they alleviate provided the best insight into the following issues: 1) the structural arrangement of a network, such as placement of servers and suboptimal routes, can contribute significantly to latency; 2) each interaction between communicating endpoints adds a Round Trip Time (RTT) to latency, especially significant for short flows; 3) in addition to base propagation delay, several sources of delay accumulate along transmission paths, today intermittently dominated by queuing delays; 4) it takes time to sense and use available capacity, with overuse inflicting latency on other flows sharing the capacity; and 5) within end systems delay sources include operating system buffering, head-of-line blocking, and hardware interaction. No single source of delay dominates in all cases, and many of these sources are spasmodic and highly variable. Solutions addressing these sources often both reduce the overall latency and make it more predictable.
Afilliation | Communication Systems, Communication Systems |
Project(s) | RITE: Reducing Internet Transport Latency |
Publication Type | Journal Article |
Year of Publication | 2014 |
Journal | IEEE Communications Surveys and Tutorials |
Volume | 18 |
Issue | 3 |
Pagination | 2149–2196 |
Date Published | 10/2016 |
Publisher | IEEE Communications Society |
ISSN | 1553-877X |
Other Numbers | ISSN: 1553-877X |
Keywords | Internet, latency, network |
URL | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6... |
DOI | 10.1109/COMST.2014.2375213 |
Proceedings, refereed
Practical Passive Shared Bottleneck Detection Using Shape Summary Statistics
In 39th Annual IEEE Conference on Local Computer Networks. IEEE, 2014.Status: Published
Practical Passive Shared Bottleneck Detection Using Shape Summary Statistics
Afilliation | Communication Systems |
Project(s) | RITE: Reducing Internet Transport Latency |
Publication Type | Proceedings, refereed |
Year of Publication | 2014 |
Conference Name | 39th Annual IEEE Conference on Local Computer Networks |
Pagination | 150--158 |
Publisher | IEEE |
Keywords | Conference |
DOI | 10.1109/LCN.2014.6925767 |
Research Notes | Open access on IEEE Xplore |
Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources

In the current and future industry and society, there will be an increasing number of systems storing and processing large amounts of data. This is the next frontier for innovation, competition and productivity with ongoing large initiatives both in the EU and the US. Areas where processing of large amounts of unstructured data is applied include medicine, meteorology, genomics,
As such, the aim of the EONS research project is to perform basic research in the area of system and tools support for both, parallel programming and parallel processing, in the context of future distributed large-scale heterogeneous systems. EONS will develop concepts and mechanisms that enable the development of software for these next-generation big-data
The following points are investigated by EONS:
- Formalization of a high level parallel programming model that is compatible with those programming models and languages that developer today know. There are already several approaches to specify potential parallelism, but for workloads with processing and/or time dependencies, we need to add notions of deadlines and execution orders.
- Compiler and multi-core run-time system. Many run-time systems have been built and are in use, but there are large potentials for more efficient execution and run-time support for the dependencies must be added. Scheduling and mapping of tasks to processing engines will here be important. At the core of this plan is the common exploitation of knowledge that can be retained from the compilation step with knowledge that can be gained at runtime during execution on a multi-core system.
- Distributed implementation and high-level scheduler optimization. Adding support for multiple machines makes the previous item more complex. The heterogeneity and complexity increase and the communication costs vary more. A high-level scheduler therefore must take this into account, i.e., in addition to the competition for resources from different concurrent workloads.
Final goal:
Funding source:
All partners:
Publications for Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources
Proceedings, refereed
OpenSea - Open Search Based Classification Tool
In Proceedings of the 9th ACM Multimedia Systems Conference. Amsterdam, Netherlands: ACM, 2018.Status: Published
OpenSea - Open Search Based Classification Tool
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Proceedings of the 9th ACM Multimedia Systems Conference |
Pagination | 363-368 |
Publisher | ACM |
Place Published | Amsterdam, Netherlands |
ISBN Number | 978-1-4503-5192-8 |
DOI | 10.1145/3204949.3208128 |
Autonomic Adaptation of Multimedia Content Adhering to Application Mobility
In Distributed Applications and Interoperable Systems (DAIS 2018). Lecture Notes in Computer Science ed. Vol. 10853. Springer, Cham, 2018.Status: Published
Autonomic Adaptation of Multimedia Content Adhering to Application Mobility
Today,manyusersofmultimediaapplicationsaresurrounded by a changing set of multimedia-capable devices. However, users can move their running multimedia applications only to a pre-defined set of devices. Application mobility is the paradigm where users can move their running applications (or parts of) to heterogeneous devices in a seamless manner. In order to continue multimedia processing under the implied context changes in application mobility, applications need to adapt the presentation of multimedia content and their internal configuration. We propose the system DAMPAT that implements an adaptation control loop to adapt multimedia pipelines. Exponential combinatorial growth of possible pipeline configurations is controlled by architectural constraints specified as high-level goals by application developers. Our evaluation shows that the pipeline only needs to be interrupted a few tens of milliseconds to perform the reconfiguration. Thus, production or consumption of multimedia content can continue across heterogeneous devices and user context changes in a seamless manner.
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources, Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Distributed Applications and Interoperable Systems (DAIS 2018) |
Volume | 10853 |
Edition | Lecture Notes in Computer Science |
Pagination | 153-168 |
Date Published | 06/2018 |
Publisher | Springer, Cham |
ISBN Number | 978-3-319-93766-3 |
DOI | 10.1007/978-3-319-93767-0_11 |
Dynamic Adaptation of Multimedia Presentations for Videoconferencing in Application Mobility
In IEEE International Conference on Multimedia and Expo (ICME). San Diego, CA, USA: IEEE, 2018.Status: Published
Dynamic Adaptation of Multimedia Presentations for Videoconferencing in Application Mobility
Application mobility is the paradigm where users can move their running applications to heterogeneous devices in a seamless manner. This mobility involves dynamic context changes of hardware, network resources, user environment, and user preferences. In order to continue multimedia processing under these context changes, applications need to adapt not only the collection of media streams, i.e., multimedia presentation, but also their internal configuration to work on different hardware. We present the performance analysis to adapt a video-conferencing prototype application in a proposed adaptation control loop to autonomously adapt multimedia pipelines. Results show that the time spent to create an adaptation plan and execute it is in the order of hundreds of milliseconds. The reconfiguration of pipelines, compared to building them from scratch, is approximately 1000 times faster when re-utilizing already instantiated hardware-dependent components. Therefore, we conclude that the adaptation of multimedia pipelines is a feasible approach for multimedia applications that adhere to application mobility.
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources, Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | IEEE International Conference on Multimedia and Expo (ICME) |
Date Published | 07/2018 |
Publisher | IEEE |
Place Published | San Diego, CA, USA |
ISSN Number | 1945-7871 |
DOI | 10.1109/ICME.2018.8486565 |
Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia
In 2018 IEEE Conference on Biomedical and Health Informatics (BHI). IEEE, 2018.Status: Published
Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia
Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos or images of examinations reach a detection performance of 16% for the detection of bleeding to 69% for the detection of angiectasia. This shows that automatic detection to support medical experts can be useful. In this paper, we present several machine learning-based approaches for angiectasia detection in wireless video capsule endoscopy frames. In summary, the most promising results for pixel-wise localization and framewise detection are obtained by the proposed deep learning method using generative adversarial networks (GANs). Using this approach, we achieve a sensitivity of 88% and specificity of 99.9% for pixel-wise localization, and a sensitivity of 98% and a specificity of 100% for frame-wise detection. Thus, the results demonstrate the capability of using deep learning for automatic angiectasia detection in real clinical settings.
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | 2018 IEEE Conference on Biomedical and Health Informatics (BHI) |
Pagination | 365-368 |
Publisher | IEEE |
Keywords | Angiectasia, computer aided diagnosis, deep learning, Machine learning, video capsular endoscopy |
DOI | 10.1109/BHI.2018.8333444 |
Talk, keynote
Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches
In IEEE Conference on Biomedical and Health Informatics (BHI) 2018, 2018.Status: Published
Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches
Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos of examinations reach a detection performance of 16% for the detection of bleeding to 69% for the detection of angiectasia. In this paper, we present several machine-learning-based approaches for angiectasia detection in wireless video capsule endoscopy images. The most promising results for pixel-wise localization and frame-wise detection are obtained by the proposed deep learning approach using generative adversarial networks (GANs) with a sensitivity of 88% and specificity of 99.9% for pixel-wise localization and a sensitivity of 98% and a specificity of 100% for frame-wise detection, which fits the requirements for automatic angiectasia detection in real clinical settings.
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources, Department of Holistic Systems |
Publication Type | Talk, keynote |
Year of Publication | 2018 |
Location of Talk | IEEE Conference on Biomedical and Health Informatics (BHI) 2018 |
Journal Article
Overview of ImageCLEF 2017: Information extraction from images
Experimental IR Meets Multilinguality, Multimodality, and Interaction. 10456 (2017): 315-337.Status: Published
Overview of ImageCLEF 2017: Information extraction from images
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Journal Article |
Year of Publication | 2017 |
Journal | Experimental IR Meets Multilinguality, Multimodality, and Interaction. |
Volume | 10456 |
Pagination | 315-337 |
Publisher | Springer International Publishing |
ISBN Number | 978-3-319-65813-1 |
Efficient disease detection in gastrointestinal videos – global features versus neural networks
Multimedia Tools and Applications 76, no. 21 (2017): 22493-22525.Status: Published
Efficient disease detection in gastrointestinal videos – global features versus neural networks
Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-to-end multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multiclass detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Journal Article |
Year of Publication | 2017 |
Journal | Multimedia Tools and Applications |
Volume | 76 |
Issue | 21 |
Pagination | 22493–22525 |
Date Published | 11/2017 |
Publisher | ACM/Springer |
ISSN | 1380-7501 |
Keywords | Algorithmic screening, Automatic disease detection, Deep learning neural networks, Global and local image features, Information retrieval, Medical, performance evaluation |
URL | https://link.springer.com/article/10.1007%2Fs11042-017-4989-y |
DOI | 10.1007/s11042-017-4989-y |
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
IEEE Transactions on Medical Imaging (2017): 1-19.Status: Published
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Journal Article |
Year of Publication | 2017 |
Journal | IEEE Transactions on Medical Imaging |
Pagination | 1-19 |
Publisher | IEEE |
ISSN | 0278-0062 |
Keywords | Endoscopic vision, Handcrafted features, Machine learning, Polyp Detection, Validation Framework |
DOI | 10.1109/TMI.2017.2664042 |
From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System
ACM Transactions on Multimedia Computing, Communications, and Applications 13, no. 3 (2017).Status: Published
From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Journal Article |
Year of Publication | 2017 |
Journal | ACM Transactions on Multimedia Computing, Communications, and Applications |
Volume | 13 |
Issue | 3 |
Publisher | ACM |
PhD Thesis
EIR - A Medical Multimedia System for Efficient Computer Aided Diagnosis
In University of Oslo. Vol. PhD. University of Oslo: University of Oslo, 2017.Status: Published
EIR - A Medical Multimedia System for Efficient Computer Aided Diagnosis
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | PhD Thesis |
Year of Publication | 2017 |
Degree awarding institution | University of Oslo |
Degree | PhD |
Date Published | 02/2017 |
Publisher | University of Oslo |
Place Published | University of Oslo |
Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization

PCI Express (PCIe) is today widely used for static local input/output (I/O) expansion and is gaining momentum as a host-to-host high speed interconnect. However these services are currently not able to integrate onto a single hardware infrastructure, two separate PCIe networks are needed.
Dolphin has developed interconnect solutions since the early 1990s and its current product line relies exclusively on PCIe-based interface cards and switches and competes with alternative high-speed communication technologies. A great advantage of Dolphin’s PCIe products is the reduced protocol overhead compared to technologies like 10Gb Ethernet, InfiniBand and other proprietary interconnect technologies. Dolphin is the market leader in providing fast, optimized and easy to use software and hardware products enabling PCIe to be used as a high speed interconnect.
I/O devices are typically statically assigned to a single root complex (host), hot-add, device migration, device sharing and remote access are not supported in flexible way.
In principle, all I/O devices on a shared PCIe fabric can be accessed directly using the PCIe Non Transparent Bridging (NTB) addressing techniques by any connected remote computer, but the required software structure, OS interfaces and implementations do not exist.
Device sharing functionality is addressed in the PCIe Single Root I/O Virtualization (SR-IOV) and Multi Root I/O Virtualization (MR-IOV) specifications but this is still a static approach and requires PCIe devices to be developed according to these specifications.
As I/O devices are normally owned by one compute node, I/O data are normally relayed to remote servers though the host using traditional networking services. Using PCIe NTB techniques, it is possible to enable I/O devices to directly transfer data to a remote node. This will significantly reduce latency and overhead during data transfers.
Final goal:
The goal with this project is to develop a new framework for the operating system and virtual machines that will enable remote discover, addressing, access and use of standard PCIe devices. The framework will enable standard PCIe devices to be re-allocated and shared by computer nodes in the PCIe network with no or minimum changes to the applications and device drivers. On top of this framework we will develop services to validate and demonstrate the framework such as a fast cluster file system, legacy device driver access to I/O devices on a remote node, and clustering of accelerator cards such as the Intel Xeon Phi co-processor and Nvidia Tesla graphics processing units.\
Funding source:
The Research Council of Norway
All partners:
Dolphin Interconnect Solutions
Publications for Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization
Proceedings, refereed
DAMPAT: Dynamic Adaptation of Multimedia Presentations in Application Mobility
In International Symposium on Multimedia (ISM). Taichung, Taiwan: IEEE, 2017.Status: Published
DAMPAT: Dynamic Adaptation of Multimedia Presentations in Application Mobility
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | International Symposium on Multimedia (ISM) |
Date Published | 12/2017 |
Publisher | IEEE |
Place Published | Taichung, Taiwan |
DOI | 10.1109/ISM.2017.56 |
A Holistic Multimedia System for Gastrointestinal Tract Disease Detection
In 8th annual ACM conference on Multimedia Systems (MMSys). ACM, 2017.Status: Published
A Holistic Multimedia System for Gastrointestinal Tract Disease Detection
Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.
In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources, Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | 8th annual ACM conference on Multimedia Systems (MMSys) |
Pagination | 112-123 |
Date Published | 06/2017 |
Publisher | ACM |
ISBN Number | 978-1-4503-5002-0 |
URL | http://dl.acm.org/citation.cfm?id=3083189 |
DOI | 10.1145/3083187.3083189 |
Load Balancing of Multimedia Workloads for Energy Efficiency on the Tegra K1 Multicore Architecture
In 8th annual ACM conference on Multimedia Systems (MMSys). ACM, 2017.Status: Published
Load Balancing of Multimedia Workloads for Energy Efficiency on the Tegra K1 Multicore Architecture
Energy efficiency is a timely topic for modern mobile computing. Reducing the energy consumption of devices not only increases their battery lifetime, but also reduces the risk of hardware failure. Many researchers strive to
understand the relationship between software activity and hardware power usage. A recurring strategy for saving power is to reduce operating frequencies. It is widely acknowledged that standard frequency scaling algorithms generally overreact to changes in hardware utilisation. More recent and original efforts attempt to balance software workloads on heterogeneous multicore architectures, such as the Tegra K1, which includes a quad-core CPU and a CUDA-capable GPU. However, it is not known whether it is possible to utilise these processor elements in parallel to save energy. Research into these types of systems are unfortunately often evaluated with the Performance Per Watt (PPW) metric, which is an unaccurate method because it ignores constant power usage from idle components. We show that this metric can end up increase energy usage on the Tegra K1, and give a false impression of how such systems consume energy. In reality, we show that it is much harder to save energy by balancing workloads between the heterogeneous cores of the Tegra K1, where we demonstrate only a 5% energy saving by offloading 10% DCT workload from the GPU to the CPU. Significantly more energy can be saved (up to 50%) using the appropriate processor for different workloads.
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | 8th annual ACM conference on Multimedia Systems (MMSys) |
Pagination | 124-135 |
Date Published | 06/2017 |
Publisher | ACM |
ISBN Number | 978-1-4503-5002-0 |
URL | http://dl.acm.org/citation.cfm?doid=3083187.3083195 |
DOI | 10.1145/3083187.3083195 |
Proceedings, refereed
SOCKMAN: Socket Migration for Multimedia Applications
In The 12th International Conference on Telecommunications (ConTEL). Zagreb, Croatia: IEEE, 2013.Status: Published
SOCKMAN: Socket Migration for Multimedia Applications
The dynamically changing set of multimedia capable devices in the vicinity of a user can be leveraged to create new ways of experiencing multimedia applications through migrating parts of running multimedia applications to the most suited devices. This paper addresses one of the core challenges of application migration, i.e., migration of transport protocol state that is maintained by the endpoints of established connections. Our solution fulfills the stringent temporal requirements of multimedia applications and enables migratable applications to interact with legacy applications, e.g., a migratable video player together with YouTube. The core idea of our solution, called SOCKMAN, is to provide a middleware service to hide that proxy-based forwarding is used to migrate connection endpoints, i.e. sockets, and to maintain an end-to-end perspective for the applications. The evaluation of the SOCKMAN implementation shows that SOCKMAN meets multimedia application requirements, preserves transport protocol state, and performs well on low-end devices, like mobile phones.
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2013 |
Conference Name | The 12th International Conference on Telecommunications (ConTEL) |
Pagination | 115-122 |
Date Published | June |
Publisher | IEEE |
Place Published | Zagreb, Croatia |
Keywords | connection handover, Multimedia, process migration |
URL | http://ieeexplore.ieee.org/document/6578279/ |
Proceedings, refereed
Migration of Fine-grained Multimedia Applications
In Middleware '12 Proceedings of the Posters and Demo Track. New York, NY, USA: ACM, 2012.Status: Published
Migration of Fine-grained Multimedia Applications
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2012 |
Conference Name | Middleware '12 Proceedings of the Posters and Demo Track |
Pagination | 12:1–12:2 |
Date Published | 12/2012 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 978-1-4503-1612-5 |
Keywords | fine-grained applications, Multimedia, process migration |
DOI | 10.1145/2405153.2405165 |
Efficient Data Sharing for Multi-device Multimedia Applications
In Proceedings of the Workshop on Multi-device App Middleware. New York, NY, USA: ACM, 2012.Status: Published
Efficient Data Sharing for Multi-device Multimedia Applications
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2012 |
Conference Name | Proceedings of the Workshop on Multi-device App Middleware |
Pagination | 2:1–2:6 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 978-1-4503-1617-0 |
Keywords | data sharing, fine-grained applications, multi-device applications, Multimedia |
URL | http://doi.acm.org/10.1145/2405172.2405174 |
DOI | 10.1145/2405172.2405174 |
Department of Holistic Systems
Our goal is to investigate and solve real-world problems in the area of intelligent distributed systems.
Today, everyone is talking about big-data analytics, video streaming, machine learning, web search, mobile apps, etc., and we all take it for granted that these "services" work perfectly. However, underneath such applications, there are large systems, and individual components of these systems are often “only” a part of a big ecosystem of integrated building blocks needed to enable a functional service. In this respect, the HOST department addresses challenges potentially covering all components of the entire system, from data creation to visualization of the results.
Using a combination of primary and applied research, we are targeting numerous applications, many in the field of sports and medicine, where we have a holistic view of the system and perform basic research, do experimental prototyping, and run experiments in the intended environments.
Currently, machine learning is an essential component of our research where we, for example, in real-time, aim to analyze athlete performance and detect diseases in medical videos. In our holistic systems view, not only the accuracy of the machine learning analysis is of importance, but also the complete pipe-line integration and the system performance (e.g., resource consumption and scalability). We architect complete systems and optimize for particular application requirements, both functional and non-functional, to provide the best possible quality of the service and the lowest possible resource consumption. Finally, we have a goal to put the results into use for the society where we contribute to open source projects and have spun off new industries.

Publications for Department of Holistic Systems
Proceedings, refereed
Predicting the degree of meibomian gland dropout with artificial intelligence
In ARVO Annual Meeting, 2023.Status: Published
Predicting the degree of meibomian gland dropout with artificial intelligence
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | ARVO Annual Meeting |
Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence
Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are commonly visualized through meibography, a technique requiring specialist equipment and knowledge that might not be available to the physician. In the present project we use machine learning on clinical tabular data to predict the degree of meibomian gland dropout. Moreover, we employ explainable artificial intelligence on the best performing algorithms for feature importance evaluation. The best performing algorithms were AdaBoost, multilayer perceptron and LightGBM which outperformed the majority vote baseline classifier in every included evaluation metric for both multioutput and binary classification. Through explainable artificial intelligence known associations are validated and novel connections identified and discussed.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
Keywords | Dry eye disease, Explainable artificial intelligence, Machine learning, meibography, meibomian gland dysfunction |
ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset
In International Conference on Multimedia Modeling (MMM). Vol. 13833. Cham: Springer International Publishing, 2023.Status: Published
ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset
Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people’s lives. One of the key challenges is that the collected data is often massive and unstructured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach, respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a structured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called ScopeSense from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International Conference on Multimedia Modeling (MMM) |
Volume | 13833 |
Pagination | 502 - 514 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-031-27076-5 |
ISSN Number | 0302-9743 |
URL | https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2https://l... |
DOI | 10.1007/978-3-031-27077-210.1007/978-3-031-27077-2_39 |
Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks
In International conference on multimedia modeling. Springer International Publishing, 2023.Status: Published
Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks
High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs’ potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients’ privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International conference on multimedia modeling |
Publisher | Springer International Publishing |
Keywords | deep learning, Medical data, synthetic data generation |
DOI | 10.1007/978-3-031-27077-2_34 |
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.
Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns. We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
Keywords | Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics |
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
In ARVO Annual Meeting, 2023.Status: Published
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | ARVO Annual Meeting |
Multimedia datasets: challenges and future possibilities
In International conference on multimedia modeling, 2023.Status: Published
Multimedia datasets: challenges and future possibilities
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International conference on multimedia modeling |
DOI | 10.1007/978-3-031-27818-1_58 |
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
In Nordic Artificial Intelligence Research and Development. Springer, 2023.Status: Published
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | Nordic Artificial Intelligence Research and Development |
Pagination | 81-93 |
Publisher | Springer |
Book
The Influence of Delay on Cloud Gaming Quality of Experience
Cham: Springer, 2022.Status: Published
The Influence of Delay on Cloud Gaming Quality of Experience
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Book |
Year of Publication | 2022 |
Edition | 1 |
Number of Pages | 156 |
Date Published | 06/2022 |
Publisher | Springer |
Place Published | Cham |
ISBN Number | 978-3-030-99868-4 |
URL | https://link.springer.com/book/10.1007/978-3-030-99869-1 |
DOI | 10.1109/QoMEX55416.2022.9900908 |
Book Chapter
Smittestopp Backend
In Smittestopp − A Case Study on Digital Contact Tracing, 29-62. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp Backend
An efficient backend solution is of great importance for any large-scale system, and Smittestopp is no exception. The Smittestopp backend comprises various components for user and device registration, mobile app data ingestion, database and cloud operations, and web interface support. This chapter describes our journey from a vague idea to a deployed system. We provide an overview of the system internals and design iterations and discuss the challenges that we faced during the development process, along with the lessons learned. The Smittestopp backend handled around 1.5 million registered devices and provided various insights and analyses before being discontinued a few months after its launch.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Book Chapter |
Year of Publication | 2022 |
Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
Volume | 11 |
Pagination | 29 - 62 |
Date Published | 06/2022 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-031-05465-5 |
ISBN | 2512-1677 |
URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2.pdf |
DOI | 10.1007/978-3-031-05466-2_3 |
Interview training of child-welfare and law-enforcement professionals interviewing maltreated children supported via artificial avatars

The interdisciplinary FRIPRO project aims to improve interviews with maltreated children through a training program using realistic and interactive child avatars.
The department of Holistic Systems (HOST) at SimulaMet will be working with the Faculty of Social Sciences at OsloMet. The project will begin on the 1st of April 2021 and end on the 31st of March in 2024. It is funded by The Research Council of Norway with 12 million NOK and will include three Ph.D. positions.
Maltreatment and abuse of children is a significant societal problem that has serious and damaging effects on children’s behavior, psychological development, and adjustment. Detection and prevention of violence and sexual abuse against children is, therefore, a high priority for Child Protective Services (CPS) and law-enforcement professionals. The conversations and investigative interviews that are conducted with these children must be of high quality. However, both Norwegian and international research shows that despite investments in methodology, the current interview and conversation skills still need to be improved.
By using an empirically informed training system in highly realistic child avatars, this project aims to develop and maintain the advanced skills needed for interviewing maltreated children. They will use data from past investigative interviews with maltreated children and create a real-looking avatar that is capable of expressing emotion and spontaneous responses.
The planned avatar will be a combination of technologies from multiple areas in computer science including AI, computer vision, and natural language processing. The aim is for the child avatars to be a part of an interview-training program that will be implemented in cooperation with the CPS and the police. The training system will be evaluated by the project scientists to judge effectiveness in relation to real-world needs.
The project also involves collaborations with researchers from Griffith University in Australia and the University of Cambridge in the United Kingdom.
Publications
Journal Article
A multi-center polyp detection and segmentation dataset for generalisability assessment
Nature Scientific Data 10 (2023).Status: Published
A multi-center polyp detection and segmentation dataset for generalisability assessment
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Nature Scientific Data |
Volume | 10 |
Publisher | Nature |
URL | https://doi.org/10.1038/s41597-023-01981-y |
DOI | 10.1038/s41597-023-01981-y |
Enhancing Questioning Skills through Child Avatar Chatbot Training with Feedback
Frontiers in Psychology (2023).Status: Published
Enhancing Questioning Skills through Child Avatar Chatbot Training with Feedback
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Frontiers in Psychology |
Publisher | Frontiers |
DOI | 10.3389/fpsyg.2023.1198235 |
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Trauma, Violence, & Abuse (2023).Status: Accepted
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Livestreaming of child sexual abuse is an established form of online child sexual exploitation
and abuse. However, only a limited body of research has examined this issue. The Covid-19
pandemic has accelerated internet use and user knowledge of livestreaming services
emphasising the importance of understanding this crime. In this scoping review, existing
literature was brought together through an iterative search of eight databases containing peer-
reviewed journal articles, as well as grey literature. Records were eligible for inclusion if the
primary focus was on livestream technology and online child sexual exploitation and abuse,
the child being defined as eighteen years or younger. Fourteen of the 2,218 records were
selected. The data were charted and divided into four categories: victims, offenders,
legislation, and technology. Limited research, differences in terminology, study design, and
population inclusion criteria present a challenge to drawing general conclusions on the
current state of livestreaming of child sexual abuse. The records show that victims are
predominantly female. The average livestream offender was found to be older than the
average online child sexual abuse offender. Therefore, it is unclear whether the findings are
representative of the global population of livestream offenders. Furthermore, there appears to
be a gap in what the records show on platforms and payment services used and current digital
trends. The lack of a legal definition and privacy considerations pose a challenge to
investigation, detection, and prosecution. The available data allow some insights into a
potentially much larger issue.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Trauma, Violence, & Abuse |
Publisher | SAGE Publications |
Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis
Diagnostics 13, no. 14 (2023).Status: Published
Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis
Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of “black box” models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Diagnostics |
Volume | 13 |
Issue | 14 |
Number | 2345 |
Date Published | 07/2023 |
Publisher | MDPI |
Keywords | electrocardiograms, Explainable artificial intelligence, heat maps |
DOI | 10.3390/diagnostics13142345 |
Proceedings, refereed
Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks
In International conference on multimedia modeling. Springer International Publishing, 2023.Status: Published
Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks
High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs’ potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients’ privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International conference on multimedia modeling |
Publisher | Springer International Publishing |
Keywords | deep learning, Medical data, synthetic data generation |
DOI | 10.1007/978-3-031-27077-2_34 |
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.
Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns. We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
Keywords | Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics |
Man vs. AI: An in silico study of polyp detection performance
In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). L'Aquila, Italy: IEEE, 2023.Status: Published
Man vs. AI: An in silico study of polyp detection performance
AI-based colon polyp detection systems have received much attention, and several products and prototypes report good results. In silico verification is a crucial step when developing such systems, but very few compare human versus AI performance. This paper, therefore, describes methods and results for an in silico test of an AI model with two different versions for polyp detection in colonoscopy and compares them to the performance of endoscopist doctors who reviewed the same colonoscopy video clips. The two versions have different thresholds for false positive rate reduction. Our models perform polyp detection within the range of the endoscopists' performance, although faster, showing a potential for use in a clinical setting. For the AI and the endoscopists alike, the results show a trade-off between high sensitivity and high specificity; to achieve perfect detection, one will also get abundance of false positives. This can cause alarm fatigue in a clinical setting.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) |
Publisher | IEEE |
Place Published | L'Aquila, Italy |
URL | https://ieeexplore.ieee.org/document/10178833 |
DOI | 10.1109/CBMS58004.2023.00307 |
Multimedia datasets: challenges and future possibilities
In International conference on multimedia modeling, 2023.Status: Published
Multimedia datasets: challenges and future possibilities
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International conference on multimedia modeling |
DOI | 10.1007/978-3-031-27818-1_58 |
Predicting the degree of meibomian gland dropout with artificial intelligence
In ARVO Annual Meeting, 2023.Status: Published
Predicting the degree of meibomian gland dropout with artificial intelligence
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | ARVO Annual Meeting |
ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset
In International Conference on Multimedia Modeling (MMM). Vol. 13833. Cham: Springer International Publishing, 2023.Status: Published
ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset
Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people’s lives. One of the key challenges is that the collected data is often massive and unstructured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach, respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a structured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called ScopeSense from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International Conference on Multimedia Modeling (MMM) |
Volume | 13833 |
Pagination | 502 - 514 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-031-27076-5 |
ISSN Number | 0302-9743 |
URL | https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2https://l... |
DOI | 10.1007/978-3-031-27077-210.1007/978-3-031-27077-2_39 |
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
In ARVO Annual Meeting, 2023.Status: Published
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | ARVO Annual Meeting |
Proceedings, refereed
A Comparative Study of Interactive Environments for Investigative Interview of A Virtual Child Avatar
In IEEE international symposium on multimedia (ISM). IEEE, 2022.Status: Published
A Comparative Study of Interactive Environments for Investigative Interview of A Virtual Child Avatar
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE international symposium on multimedia (ISM) |
Pagination | 194-201 |
Publisher | IEEE |
DOI | 10.1109/ISM55400.2022.00043 |
Assisting Soccer Game Summarization via Audio Intensity Analysis of Game Highlights
In Proceedings of 12th IOE Graduate Conference. Vol. 12. Institute of Engineering, Tribhuvan University, Nepal, 2022.Status: Published
Assisting Soccer Game Summarization via Audio Intensity Analysis of Game Highlights
In association football, the development of multimodal summaries is of great importance to both broadcasters and spectators since a large number of viewers choose to follow just the soccer game highlights. The fundamental drive for the development of summarization systems is the requirement to manage huge amounts of data in different formats. By highlighting the most pertinent facts and limiting or omitting unnecessary aspects, summarization helps avoid "information overload." The properties of the audio signals during a particular event can be used to calculate excitement around that event and filter events based on their importance. A root-mean-square (RMS) analysis of audio events was carried out to analyse the excitement across the events in the SoccerNet dataset. It was clearly seen that important events with excitement have a high and distinguishable RMS audio intensity. It was also observed that the generated noise of the crowd was significantly different across various events and if it happened for the home or away team. The intensity was higher for events related to the home team. Likewise, as the wavelet has the benefit of integrating a wave with a specific period, Morlet wavelet analysis was performed for various event types, and the power of the signal across various wavelet scales was analyzed. A distinct signature across various wavelet scales was observed for different events.
Afilliation | Software Engineering, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of 12th IOE Graduate Conference |
Volume | 12 |
Pagination | 25 – 32 |
Date Published | October |
Publisher | Institute of Engineering, Tribhuvan University, Nepal |
Keywords | association football, audio signal, soccer game highlights, summarization |
URL | http://conference.ioe.edu.np/publications/ioegc12/IOEGC-12-004-12009.pdf |
DOI | 10.13140/RG.2.2.34457.70240/1 |
Automatic Thumbnail Selection for Soccer Videos using Machine Learning
In Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22). ACM, 2022.Status: Published
Automatic Thumbnail Selection for Soccer Videos using Machine Learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22) |
Pagination | 73-85 |
Publisher | ACM |
DOI | 10.1145/3524273.3528182 |
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
In NAIS: Symposium of the Norwegian AI Society . Vol. 1650. NAIS 2022, 2022.Status: Published
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
The in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | NAIS: Symposium of the Norwegian AI Society |
Volume | 1650 |
Pagination | 1-11 |
Publisher | NAIS 2022 |
ISBN Number | 978-3-031-17029-4 |
Keywords | clustering, Computer Vision and Pattern Recognition (cs.CV), human reproduction, medical videos, Unsupervised learning |
DOI | 10.1007/978-3-031-17030-0_9 |
Comparison of Crowdsourced and Remote Subjective User Studies: A Case Study of Investigative Child Interviews
In The 14th International Conference on Quality of Multimedia Experience. IEEE, 2022.Status: Published
Comparison of Crowdsourced and Remote Subjective User Studies: A Case Study of Investigative Child Interviews
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | The 14th International Conference on Quality of Multimedia Experience |
Publisher | IEEE |
URL | https://ieeexplore.ieee.org/document/9900900 |
DOI | 10.1109/QoMEX55416.2022.9900900 |
Estimating Predictive Uncertainty in Gastrointestinal Polyp Segmentation
In 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). Shenzen, China: IEEE, 2022.Status: Published
Estimating Predictive Uncertainty in Gastrointestinal Polyp Segmentation
Deep neural networks have achieved state-of-the-art performance on numerous applications in the medical field, with use-cases ranging from automation of mundane tasks to diagnosis of life-threatening diseases. Despite these achievements, deep neural networks are considered “black boxes” due to their complex structure and general lack of transparency in their decision-making process. These attributes make it challenging to incorporate deep learning into existing clinical workflows as decisions often need more support than blind faith in a statistical model. This paper presents an investigation of uncertainty estimation for the detection of colon polyps using deep convolutional neural networks (CNNs). We experiment with two different approaches to measure uncertainty, Monte Carlo (MC) dropout and deep ensembles, and discuss the advantages and disadvantages of both methods in terms of computational efficiency and performance gain. Furthermore, we apply the two uncertainty methods to two different state-of-the-art CNN-based polyp segmentation architectures. The uncertainty is visualized as heatmaps on the input images and can be used to make more informed decisions on whether or not to trust a model's predictions. The results show that the predictive uncertainties provide a comparison between different models' predictions which can be interpreted as contrastive explanations where the values are largely influenced by the degree of independence between the models in the ensemble. We also reveal that MC dropout is shown to lack at providing contrastive uncertainty values due to the high correlation between the models' in the ensemble.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) |
Pagination | 44-49 |
Publisher | IEEE |
Place Published | Shenzen, China |
URL | https://ieeexplore.ieee.org/document/9867012/http://xplorestaging.ieee.o... |
DOI | 10.1109/CBMS55023.2022.00015 |
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework
In 2022 IEEE International Symposium on Multimedia (ISM). Italy: IEEE, 2022.Status: Published
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework
Subjective user studies are important to ensure the fidelity and usability of systems that generate multimedia content. Testing how end-users and domain experts perceive multimedia assets might provide crucial information. In this paper, we present our experiences with the open source hybrid crowdsourced-remote user survey framework called Huldra, which is intended for conducting web-based subjective user studies and aims to integrate the individual benefits associated with traditional, crowdsourced, and remote methods. We disseminate our experiences and insights from two actively deployed use cases and discuss challenges and opportunities associated with using Huldra as a framework for conducting user studies.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 2022 IEEE International Symposium on Multimedia (ISM) |
Pagination | 161-162 |
Publisher | IEEE |
Place Published | Italy |
URL | https://ieeexplore.ieee.org/document/10019678 |
DOI | 10.1109/ISM55400.2022.00035 |
Explainability methods for machine learning systems for multimodal medical datasets: research proposal
In ACM Multimedia Systems (MMSys) Conference. ACM, 2022.Status: Published
Explainability methods for machine learning systems for multimodal medical datasets: research proposal
This paper contains the research proposal of Andrea M. Storås that was presented at the MMSys 2022 doctoral symposium. Machine learning models have the ability to solve medical tasks with a high level of performance, e.g., classifying medical videos and detecting anomalies using different sources of data. However, many of these models are highly complex and difficult to understand. Lack of interpretability can limit the use of machine learning systems in the medical domain. Explainable artificial intelligence provides explanations regarding the models and their predictions. In this PhD project, we develop machine learning models for automatic analysis of medical data and explain the results using established techniques from the field of explainable artificial intelligence. Current research indicate that there are still open issues to be solved in order for end users to understand multimedia systems powered by machine learning. Consequently, new explanation techniques will also be developed. Different types of medical data are applied in order to investigate the generalizability of the methods.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | ACM Multimedia Systems (MMSys) Conference |
Pagination | 347-351 |
Publisher | ACM |
ISBN Number | 978-1-4503-9283-9/22/06 |
DOI | 10.1145/3524273.3533925 |
HOST-ATS: Automatic Thumbnail Selection with Dashboard-Controlled ML Pipeline and Dynamic User Survey
In Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22). ACM, 2022.Status: Published
HOST-ATS: Automatic Thumbnail Selection with Dashboard-Controlled ML Pipeline and Dynamic User Survey
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22) |
Pagination | 334-340 |
Publisher | ACM |
DOI | 10.1145/3524273.3532908 |
Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets
In Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22). ACM, 2022.Status: Published
Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22) |
Pagination | 203-209 |
Publisher | ACM |
DOI | 10.1145/3524273.3532887 |
Human vs. GPT-3: The challenges of extracting emotions from child responses
In The 14th International Conference on Quality of Multimedia Experience. IEEE, 2022.Status: Published
Human vs. GPT-3: The challenges of extracting emotions from child responses
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | The 14th International Conference on Quality of Multimedia Experience |
Publisher | IEEE |
ISBN Number | 978-1-6654-8794-8 |
ISSN Number | 2472-7814 |
Accession Number | 22114185 |
URL | https://ieeexplore.ieee.org/document/9900885 |
DOI | 10.1109/QoMEX55416.2022.9900885 |
Investigative Interviews using a Multimodal Virtual Avatar
In American Psychology-Law Society Conference 2022. Denver USA,: American Psychology-Law Society, 2022.Status: Accepted
Investigative Interviews using a Multimodal Virtual Avatar
To meet best-practice standards, we are developing an interactive virtual avatar aiming as a training tool to raise interviewing skills of child-welfare and law-enforcement professionals. Therefore, we present the “Ilma” avatar that recognizes interviewers’ behavior during open-ended, closed and leading questions, and which can automatically respond to the conversation. We conducted a user study in which master students (N=3) and child protective workers (N=8) interviewed “Ilma” and rated their perception of the interaction. The results show that the participants valued the interaction and found the avatar useful. Thus, it has great potential to be an effective training tool.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | American Psychology-Law Society Conference 2022 |
Publisher | American Psychology-Law Society |
Place Published | Denver USA, |
Is More Realistic Better? A Comparison of Game Engine and GAN-based Avatars for Investigative Interviews of Children
In ICDAR '22: Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval. New York, NY, USA: ACM, 2022.Status: Published
Is More Realistic Better? A Comparison of Game Engine and GAN-based Avatars for Investigative Interviews of Children
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | ICDAR '22: Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval |
Pagination | 41-49 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450392419 |
URL | https://dl.acm.org/doi/proceedings/10.1145/3512731 |
DOI | 10.1145/351273110.1145/3512731.3534209 |
Multimedia streaming analytics: quo vadis?
In MHV '22: Mile-High Video Conference. Denver, Colorado, USA: ACM, 2022.Status: Published
Multimedia streaming analytics: quo vadis?
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | MHV '22: Mile-High Video Conference |
Pagination | 62 - 69 |
Publisher | ACM |
Place Published | Denver, Colorado, USA |
ISBN Number | 9781450392228 |
URL | https://dl.acm.org/doi/10.1145/3510450.3517321 |
DOI | 10.1145/3510450.3517321 |
Njord: a fishing trawler dataset
In Proceedings of the 13th ACM Multimedia Systems Conference (MMSYS). New York, NY, USA: ACM, 2022.Status: Published
Njord: a fishing trawler dataset
Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the 13th ACM Multimedia Systems Conference (MMSYS) |
Date Published | 08/2022 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450392839 |
URL | https://dl.acm.org/doi/pdf/10.1145/3524273.3532886 |
DOI | 10.1145/3524273.3532886 |
Parallel feature selection based on the trace ratio criterion
In International Joint Conference on Neural Networks (IJCNN). IEEE, 2022.Status: Published
Parallel feature selection based on the trace ratio criterion
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | International Joint Conference on Neural Networks (IJCNN) |
Publisher | IEEE |
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
In IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2022.Status: Published
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) |
Pagination | 66-71 |
Date Published | 07/2022 |
Publisher | IEEE |
DOI | 10.1109/CBMS55023.2022.00019 |
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning
In 35th IEEE CBMS International Symposium on Computer-Based Medical Systems. IEEE, 2022.Status: Published
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning
Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 35th IEEE CBMS International Symposium on Computer-Based Medical Systems |
Pagination | 38-43 |
Publisher | IEEE |
Keywords | Machine learning, personalized medicine, transplantation |
DOI | 10.1109/CBMS55023.2022.00014 |
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
In 2022 IEEE International Symposium on Multimedia (ISM). IEEE, 2022.Status: Published
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 2022 IEEE International Symposium on Multimedia (ISM) |
Pagination | 42-49 |
Publisher | IEEE |
DOI | 10.1109/ISM55400.2022.00012 |
Soccer Game Summarization using Audio Commentary, Metadata, and Captions
In NarSUM '22: Proceedings of the 1st Workshop on User-centric Narrative Summarization of Long Videos. New York, NY, USA: ACM, 2022.Status: Published
Soccer Game Summarization using Audio Commentary, Metadata, and Captions
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | NarSUM '22: Proceedings of the 1st Workshop on User-centric Narrative Summarization of Long Videos |
Pagination | 13-22 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450394932 |
URL | https://doi.org/10.1145/3552463.3557019 |
DOI | 10.1145/3552463.3557019 |
Towards an AI-driven talking avatar in virtual reality for investigative interviews of children
In GameSys '22: Proceedings of the 2nd Workshop on Games Systems. New York, NY, USA: ACM, 2022.Status: Published
Towards an AI-driven talking avatar in virtual reality for investigative interviews of children
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | GameSys '22: Proceedings of the 2nd Workshop on Games Systems |
Pagination | 9-15 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450393812 |
URL | https://dl.acm.org/doi/10.1145/3534085.3534340 |
DOI | 10.1145/353408510.1145/3534085.3534340 |
Video Analytics in Elite Soccer: A Distributed Computing Perspective
In IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). Trondheim, Norway: IEEE, 2022.Status: Published
Video Analytics in Elite Soccer: A Distributed Computing Perspective
Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA's 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) |
Pagination | 221-225 |
Date Published | 06/2022 |
Publisher | IEEE |
Place Published | Trondheim, Norway |
Keywords | analytics, football, soccer, Video |
URL | https://ieeexplore.ieee.org/document/9827827 |
DOI | 10.1109/SAM53842.2022.9827827 |
Virtual Reality Talking Avatar for Investigative Interviews of Maltreat Children
In 19th International Conference on Content-based Multimedia Indexing. New York, NY, USA: Association for Computing Machinery (ACM), 2022.Status: Published
Virtual Reality Talking Avatar for Investigative Interviews of Maltreat Children
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 19th International Conference on Content-based Multimedia Indexing |
Pagination | 201-204 |
Publisher | Association for Computing Machinery (ACM) |
Place Published | New York, NY, USA |
ISBN Number | 9781450397209 |
URL | https://doi.org/10.1145/3549555.3549572 |
DOI | 10.1145/3549555.3549572 |
When Every Millisecond Counts: The Impact of Delay in VR Gaming
In The 14th International Conference on Quality of Multimedia Experience. IEEE, 2022.Status: Published
When Every Millisecond Counts: The Impact of Delay in VR Gaming
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | The 14th International Conference on Quality of Multimedia Experience |
Publisher | IEEE |
URL | https://ieeexplore.ieee.org/document/9900908 |
DOI | 10.1109/QoMEX55416.2022.9900908 |
Miscellaneous
ACM Multimedia Grand Challenge on Detecting Cheapfakes
ACM Multimedia Conference (MM): ACM, 2022.Status: Published
ACM Multimedia Grand Challenge on Detecting Cheapfakes
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Miscellaneous |
Year of Publication | 2022 |
Publisher | ACM |
Place Published | ACM Multimedia Conference (MM) |
Notes | https://2022.acmmm.org/call-for-grand-challenge-submissions/ |
URL | https://detecting-cheapfakes.github.io |
MMSys'22 Grand Challenge on AI-based Video Production for Soccer
arXiv, 2022.Status: Published
MMSys'22 Grand Challenge on AI-based Video Production for Soccer
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Miscellaneous |
Year of Publication | 2022 |
Publisher | arXiv |
Other Numbers | arXiv:2202.01031 |
URL | https://arxiv.org/abs/2202.01031 |
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
arXiv, 2022.Status: Published
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Miscellaneous |
Year of Publication | 2022 |
Publisher | arXiv |
URL | https://arxiv.org/abs/2204.00617 |
DOI | 10.48550/arXiv.2204.00617 |
Journal Article
Áika: A Distributed Edge System for AI Inference
Big Data and Cognitive Computing 6, no. 2 (2022): 68.Status: Published
Áika: A Distributed Edge System for AI Inference
Video monitoring and surveillance of commercial fisheries in world oceans has been proposed by the governing bodies of several nations as a response to crimes such as overfishing. Traditional video monitoring systems may not be suitable due to limitations in the offshore fishing environment, including low bandwidth, unstable satellite network connections and issues of preserving the privacy of crew members. In this paper, we present Áika, a robust system for executing distributed Artificial Intelligence (AI) applications on the edge. Áika provides engineers and researchers with several building blocks in the form of Agents, which enable the expression of computation pipelines and distributed applications with robustness and privacy guarantees. Agents are continuously monitored by dedicated monitoring nodes, and provide applications with a distributed checkpointing and replication scheme. Áika is designed for monitoring and surveillance in privacy-sensitive and unstable offshore environments, where flexible access policies at the storage level can provide privacy guarantees for data transfer and access.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Big Data and Cognitive Computing |
Volume | 6 |
Issue | 2 |
Pagination | 68 |
Date Published | Jan-06-2022 |
Publisher | MDPI |
URL | https://www.mdpi.com/2504-2289/6/2/68 |
DOI | 10.3390/bdcc6020068 |
Artificial intelligence in dry eye disease
The Ocular Surface 23 (2022): 74-86.Status: Published
Artificial intelligence in dry eye disease
Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | The Ocular Surface |
Volume | 23 |
Pagination | 74 - 86 |
Date Published | Jan-01-2022 |
Publisher | Elsevier |
ISSN | 15420124 |
Keywords | artificial intelligence, Dry eye disease, Machine learning |
URL | https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324 |
DOI | 10.1016/j.jtos.2021.11.004 |
Automatic Tracking of the ICSI procedure using Deep Learning
Human Reproduction 37, no. Supplement_1 (2022).Status: Published
Automatic Tracking of the ICSI procedure using Deep Learning
Study question
Can deep learning be used to detect and track spermatozoa and the different parts of an ICSI procedure?
Summary answer
Deep learning can be used as a tool to assist and organize the contents of an ICSI procedure.
What is known already
Sperm tracking has been a topic of research and practice for many years, especially in the context of computer-aided sperm analysis (CASA). Recent studies have proposed using deep learning algorithms to track spermatozoa for spermatozoon selection in human and animal samples. One critical part of performing ICSI involves the selection of the “best” spermatozoon for injection, but other parts of the procedure may also be of importance. However, as far as we know, tracking using deep learning has not been applied to the ICSI procedure, where detecting instruments and the oocyte could also be helpful in post-analysis and training.
Study design, size, duration
The study was performed using three anonymized videos of the ICSI procedure. The frames of the videos were manually annotated by data scientists and verified by an embryologist. The annotations were bounding boxes around specific parts of the ICSI procedure, including sperm, pipettes, and the oocyte. We trained a YOLOv5 model on the collected data, where two videos were used for training and one video for validation.
Participants/materials, setting, methods
The videos of the ICSI procedure were captured at 200x magnification with a DeltaPix camera at Fertilitetssenteret in Oslo, Norway. ICSI was performed using a Nikon ECLIPSE TE2000-S microscope connected with Eppendorf TransferMan 4m micromanipulators. The spermatozoa were immobilised in 5 µl Polyvinylpyrrolidone (PVP; CooperSurgical). The videos had a resolution of 1920x1080 and were resized to 640x640 before being processed by the YOLOv5 model. The data will be made public in a later study.
Main results and the role of chance
Mean average precision (mAP) with the threshold of 0.5 (mAP@.5) is the main quantitative parameter measured in the YOLOv5 model. All the experiments were performed using three-fold cross-validation, where we present the average metrics calculated over the three folds. Overall, the method showed an average mAP@.5 of 0.50 across all predicted classes, which means that the method can track the different components with good accuracy. Looking closer at the individual classes, we see that instruments like the holding pipette and ICSI pipette are detected with high accuracy with a mAP@.5 of 0.87 and 0.94, respectively. The oocyte is also easily tracked with a mAP@.5 of 0.92. The first polar body is well detected with a mAP@.5 of 0.65. The model has issues detecting and tracking individual sperm (both outside and within the pipette), where the method achieved a mAP@.5 of 0.46 for tracking sperm outside the pipette and 0.03 for the sperm inside the pipette. The low score of detecting the sperm in the pipette can be explained by the often unclear visibility of the sperm through the pipette and the low number of training samples.
Limitations, reasons for caution
The limited sample size makes the generalizability of the method difficult to determine. A more extensive evaluation is necessary. Moreover, as the currency study focuses on tracking, patient information and clinical outcome were not included in the analysis.
Wider implications of the findings
Deep learning has the potential to aid embryologists to perform successful ICSI through tracking and detection of spermatozoa, pipettes, and the oocyte. This could potentially lead to better internal quality control and teaching possibilities, and hopefully better results.
Trial registration number
not applicable
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Human Reproduction |
Volume | 37 |
Issue | Supplement_1 |
Date Published | 07/2022 |
Publisher | Oxford University Press |
ISSN | 0268-1161 |
URL | https://academic.oup.com/humrep/article/37/Supplement_1/deac107.261/6619904 |
DOI | 10.1093/humrep/deac107.261 |
Exploration of Different Time Series Models for Soccer Athlete Performance Prediction
MDPI Engineering Proceedings 18, no. 1 (2022): 37.Status: Published
Exploration of Different Time Series Models for Soccer Athlete Performance Prediction
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | MDPI Engineering Proceedings |
Volume | 18 |
Issue | 1 |
Pagination | 37 |
Publisher | MDPI |
DOI | 10.3390/engproc2022018037 |
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
IEEE Transactions on Neural Networks and Learning Systems (2022): 1-14.Status: Published
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Pagination | 1 - 14 |
Date Published | Jan-01-2022 |
Publisher | IEEE |
ISSN | 2162-237X |
URL | https://ieeexplore.ieee.org/document/9741842 |
DOI | 10.1109/TNNLS.2022.3159394 |
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Computers in Biology and Medicine 143 (2022): 105227.Status: Published
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Computers in Biology and Medicine |
Volume | 143 |
Pagination | 105227 |
Date Published | Jan-04-2022 |
Publisher | Elsevier |
ISSN | 00104825 |
URL | https://linkinghub.elsevier.com/retrieve/pii/S0010482522000191 |
DOI | 10.1016/j.compbiomed.2022.105227 |
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
IEEE Journal of Biomedical and Health Informatics 26, no. 5 (2022): 2252-2263.Status: Published
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems. In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block. Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion. This allows the preservation of resolution, improved information flow and propagation of both high- and low-level features to obtain accurate segmentation maps. The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting edge medical image segmentation methods on four publicly available datasets. We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively. We further conducted generalizability tests that also achieved the highest DSC score with 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Volume | 26 |
Issue | 5 |
Pagination | 2252-2263 |
Date Published | 12/2021 |
Publisher | IEEE |
ISSN | 2168-2194 |
URL | https://ieeexplore.ieee.org/document/9662196 |
DOI | 10.1109/JBHI.2021.3138024 |
On evaluation metrics for medical applications of artificial intelligence
Scientific Reports 12 (2022): 1-9.Status: Published
On evaluation metrics for medical applications of artificial intelligence
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Scientific Reports |
Volume | 12 |
Number | 1 |
Pagination | 1–9 |
Publisher | Nature Publishing Group |
Prediction of Schizophrenia from Activity Data using Hidden Markov Model Parameters
Neural Computing and Applications (2022).Status: Published
Prediction of Schizophrenia from Activity Data using Hidden Markov Model Parameters
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Neural Computing and Applications |
Publisher | Springer |
Real-time deep learning based multi object tracking of spermatozoa in fresh samples
Human Reproduction 37, no. Supplement_1, July 2022, deac107.104 (2022).Status: Published
Real-time deep learning based multi object tracking of spermatozoa in fresh samples
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Human Reproduction |
Volume | 37 |
Issue | Supplement_1, July 2022, deac107.104 |
Date Published | 06/2022 |
Publisher | Oxford University Press |
DOI | 10.1093/humrep/deac107.104 |
SinGAN-Seg: Synthetic training data generation for medical image segmentation
PLOS ONE 17, no. 5 (2022): e0267976.Status: Published
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | PLOS ONE |
Volume | 17 |
Issue | 5 |
Pagination | e0267976 |
Date Published | 05/2022 |
Publisher | PLOS |
URL | https://doi.org/10.1371/journal.pone.0267976 |
DOI | 10.1371/journal.pone.0267976 |
Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children
Big Data and Cognitive Computing 6, no. 2 (2022): 62.Status: Published
Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Big Data and Cognitive Computing |
Volume | 6 |
Issue | 2 |
Pagination | 62 |
Date Published | Jan-06-2022 |
Publisher | MDPI |
URL | https://www.mdpi.com/2504-2289/6/2/62https://www.mdpi.com/2504-2289/6/2/... |
DOI | 10.3390/bdcc6020062 |
Towards the Neuroevolution of Low-level artificial general intelligence
Frontiers in Robotics and AI 9 (2022).Status: Published
Towards the Neuroevolution of Low-level artificial general intelligence
In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically- inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Frontiers in Robotics and AI |
Volume | 9 |
Date Published | 10/2022 |
Publisher | Frontiers |
URL | https://www.frontiersin.org/articles/10.3389/frobt.2022.1007547/full |
DOI | 10.3389/frobt.2022.1007547 |
Visual Sentiment Analysis from Disaster Images in Social Media
Sensors 22 (2022): 3628.Status: Published
Visual Sentiment Analysis from Disaster Images in Social Media
The increasing popularity of social networks and users’ tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people’s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Sensors |
Volume | 22 |
Number | 10 |
Pagination | 3628 |
Date Published | 05/2022 |
Publisher | MDPI |
URL | https://doi.org/10.3390/s22103628 |
DOI | 10.3390/s22103628 |
Poster
Automatic Thumbnail Selection for Soccer using Machine Learning
NORA Annual Conference, Stavanger, Norway, 2022.Status: Accepted
Automatic Thumbnail Selection for Soccer using Machine Learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2022 |
Place Published | NORA Annual Conference, Stavanger, Norway |
Predicting drug exposure in kidney transplanted patients using machine learning
NORA Annual Conference, Stavanger, Norway, 2022.Status: Published
Predicting drug exposure in kidney transplanted patients using machine learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2022 |
Place Published | NORA Annual Conference, Stavanger, Norway |
Type of Work | Poster presentation |
Talks, invited
Explainable Artificial Intelligence in Medicine
In Nordic AI Meet 2022, 2022.Status: Accepted
Explainable Artificial Intelligence in Medicine
Machine learning (ML) has shown outstanding abilities to solve a large variety of tasks such as image recognition and natural language processing, which has huge relevance for the medical field. Complex ML models, including convolutional neural networks (CNNs), are used to analyse high dimensional data such as images and videos from medical examinations. With increasing model complexity, the demand for techniques improving human understanding of the ML models also increases. If medical doctors do not understand how the models work, they might not know when the models are actually wrong or even refuse to use them. This can hamper the implementation of ML systems in the clinic and negatively affect patients. To promote successful integration of ML systems in the clinic, it is important to provide explanations that establish trust in the models among healthcare personnel. Explainable artificial intelligence (XAI) aims to provide explanations about ML models and their predictions. Several techniques have already been developed. Existing XAI methods often fail to meet the requirements of medical doctors, probably because they are not sufficiently involved in the development of the methods. We develop ML models solving tasks in various medical domains. The resulting models are explained using a selection of existing XAI methods, and the explanations are evaluated by medical experts. Their feedback is used to develop improved XAI methods. We have investigated established techniques for making ML systems more transparent in the fields of gastroenterology, assisted reproductive technology, organ transplantation and cardiology. Experiences from our projects will be used to develop new explanation techniques for clinical practice in close collaboration with medical experts.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Nordic AI Meet 2022 |
Keywords | Explainable artificial intelligence, Machine learning, medicine |
Soccer Athlete Performance Prediction using Time Series Analysis
In NORA Annual Conference, 2022.Status: Accepted
Soccer Athlete Performance Prediction using Time Series Analysis
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | NORA Annual Conference |
Talk, keynote
From a need, to an idea, to a complete system: a perspective based on real-world applications
In IEEE/ACM International Symposium on Quality of Service (virtual). IEEE, 2022.Status: Published
From a need, to an idea, to a complete system: a perspective based on real-world applications
Researchers and developers have been talking about what determines a good system in various aspects for a long time, and eventually, it is all about quality. We are interested in quality throughout the entire holistic system, from data production to data consumption, and we have for several years aimed to solve real-world problems with a focus on both functional and non-functional requirements. This includes optimizing the various steps of both workflow and computing pipelines.
In this talk, we will address quality at various levels and aspects within a holistic system. We will highlight some examples starting from challenges in society, using sports and health as showcases, via researching solutions where we optimize workflows, system performance and user experiences, to observations of real use and deployments of the systems. In particular, the talk will cover a video and metadata system for commercial sport production and dissemination as an integrated, next generation live streaming and archive system. We will also use an example from an AI-based medical computer assisted diagnosis system to detect anomalies during colonoscopy examinations. The talk will look at various solutions and the state of the art, and will conclude with various open challenges.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Talk, keynote |
Year of Publication | 2022 |
Location of Talk | IEEE/ACM International Symposium on Quality of Service (virtual) |
Date Published | 06/2022 |
Publisher | IEEE |
Type of Talk | Keynote |
URL | https://iwqos2022.ieee-iwqos.org/program/keynote/ |
Book Chapter
Smittestopp analytics: Analysis of position data
In Smittestopp − A Case Study on Digital Contact Tracing, 63-79. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp analytics: Analysis of position data
Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS data can offer. Did the contact happen on a bus? In a building? And of which type? Are some places recurrent contact locations? By answering such questions, GPS data can help develop more accurate and better-informed contact tracing applications. This chapter describes the ideas and approaches implemented for GPS data within the Smittestopp contact tracing application.We will present the pipeline used and the contribution of GPS data for contextual information, using inferred transport modes and surrounding POIs, showcasing the opportunities in the use of GPS information. Finally,we discuss ethical and privacy considerations, as well as some lessons learned.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Book Chapter |
Year of Publication | 2022 |
Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
Volume | 11 |
Pagination | 63 - 79 |
Date Published | 06/2022 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-031-05465-5 |
ISBN | 2512-1677 |
URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2_4 |
DOI | 10.1007/978-3-031-05466-2_4 |
Smittestopp Backend
In Smittestopp − A Case Study on Digital Contact Tracing, 29-62. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp Backend
An efficient backend solution is of great importance for any large-scale system, and Smittestopp is no exception. The Smittestopp backend comprises various components for user and device registration, mobile app data ingestion, database and cloud operations, and web interface support. This chapter describes our journey from a vague idea to a deployed system. We provide an overview of the system internals and design iterations and discuss the challenges that we faced during the development process, along with the lessons learned. The Smittestopp backend handled around 1.5 million registered devices and provided various insights and analyses before being discontinued a few months after its launch.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Book Chapter |
Year of Publication | 2022 |
Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
Volume | 11 |
Pagination | 29 - 62 |
Date Published | 06/2022 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-031-05465-5 |
ISBN | 2512-1677 |
URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2.pdf |
DOI | 10.1007/978-3-031-05466-2_3 |
Journal Article
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
Medical Image Analysis 70 (2021): 102007.Status: Published
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
Gastrointestinal endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Medical Image Analysis |
Volume | 70 |
Pagination | 102007 |
Publisher | Elsevier |
Keywords | Artificial intelligence, BioMedia 2019 Grand Challenge, Computer-aided detection and diagnosis, Gastrointestinal endoscopy challenges, Medical imaging, Medico Task 2017, Medico Task 2018 |
DOI | 10.1016/j.media.2021.102007 |
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
IEEE Journal of Biomedical and Health Informatics 25, no. 6 (2021): 2029-2040.Status: Published
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA). We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib PolypDB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other State-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF andTTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model’s performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist,196sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Volume | 25 |
Issue | 6 |
Pagination | 2029 - 2040 |
Publisher | IEEE |
Keywords | colonoscopy, conditional random field, generalization, Polyp segmentation, ResUNet++, test-time augmentation |
DOI | 10.1109/JBHI.2021.3049304 |
AI-Based Video Clipping of Soccer Events
Machine Learning and Knowledge Extraction 3, no. 4 (2021): 990-1008.Status: Published
AI-Based Video Clipping of Soccer Events
The current gold standard for extracting highlight clips from soccer games is the use of manual annotations and clippings, where human operators define the start and end of an event and trim away the unwanted scenes. This is a tedious, time-consuming, and expensive task, to the extent of being rendered infeasible for use in lower league games. In this paper, we aim to automate the process of highlight generation using logo transition detection, scene boundary detection, and optional scene removal. We experiment with various approaches, using different neural network architectures on different datasets, and present two models that automatically find the appropriate time interval for extracting goal events. These models are evaluated both quantitatively and qualitatively, and the results show that we can detect logo and scene transitions with high accuracy and generate highlight clips that are highly acceptable for viewers. We conclude that there is considerable potential in automating the overall soccer video clipping process.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Machine Learning and Knowledge Extraction |
Volume | 3 |
Issue | 4 |
Pagination | 990 - 1008 |
Date Published | 12/2021 |
Publisher | MDPI |
URL | https://www.mdpi.com/2504-4990/3/4/49/pdf |
DOI | 10.3390/make3040049 |
Artificial intelligence in the fertility clinic: status, pitfalls and possibilities
Human Reproduction 36, no. 9 (2021): 2429-2442.Status: Published
Artificial intelligence in the fertility clinic: status, pitfalls and possibilities
In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Human Reproduction |
Volume | 36 |
Issue | 9 |
Pagination | 2429 - 2442 |
Date Published | 07/2021 |
Publisher | Oxford Academic |
ISSN | 0268-1161 |
URL | https://academic.oup.com/humrep/article/36/9/2429/6330662 |
DOI | 10.1093/humrep/deab168 |
Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities
Machine Learning and Knowledge Extraction 3, no. 4 (2021): 1030-1054.Status: Published
Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities
Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Machine Learning and Knowledge Extraction |
Volume | 3 |
Issue | 4 |
Pagination | 1030 - 1054 |
Date Published | 12/2021 |
Publisher | MDPI |
URL | https://www.mdpi.com/2504-4990/3/4/51/pdf |
DOI | 10.3390/make3040051 |
Deep learning neural network can measure ECG intervals and amplitudes accurately
Journal of Electrocardiology 69 (2021): 82.Status: Published
Deep learning neural network can measure ECG intervals and amplitudes accurately
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Journal of Electrocardiology |
Volume | 69 |
Pagination | 82 |
Publisher | Elsevier |
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
Nature Scientific Reports 11 (2021): 21896.Status: Published
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Nature Scientific Reports |
Volume | 11 |
Pagination | 21896 |
Date Published | 09/2021 |
Publisher | Springer nature |
URL | https://www.nature.com/articles/s41598-021-01295-2 |
DOI | 10.1038/s41598-021-01295-2 |
DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine
Scientific Reports (2021).Status: Accepted
DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine
Recent global developments underscore the prominent role big data have in modern medical science. Privacy issues are a prevalent problem for collecting and sharing data between researchers. Synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue.In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs.In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by addressing the relevant privacy issues in medical datasets.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialN/AFunding StatementThis work is funded in part by Novo Nordisk Foundation project number NNF18CC0034900.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:We confirm that all experiments were performed in accordance with Helsinki guidelines and regulations of the Danish Regional Committees for Medical and Health Research Ethics. The data studies were approved by the ethical committee of Region Zealand (SJ-113, SJ-114, SJ-191), ethical committee of Copenhagen Amt (KA 98 155).All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe Normal DeepFake ECGs are available at OSF (https://osf.io/6hved/) with corresponding MUSE 12SL ground truth values freely downloadable and usable for ECG algorithm development. The DeepFake generative model is available at https://pypi.org/project/deepfake-ecg/ to generate only synthetic ECGs. https://osf.io/6hved/ https://pypi.org/project/deepfake-ecg/
Afilliation | Scientific Computing, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Scientific Reports |
Publisher | Nature Publishing Group |
URL | https://www.medrxiv.org/content/early/2021/05/10/2021.04.27.21256189.1 |
DOI | 10.1101/2021.04.27.21256189 |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
Scientific Reports 11 (2021): 10949.Status: Published
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Scientific Reports |
Volume | 11 |
Pagination | 10949 |
Date Published | 05/2021 |
Publisher | Springer Nature |
URL | http://www.nature.com/articles/s41598-021-90285-5h |
DOI | 10.1038/s41598-021-90285-5 |
File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments
Information 12, no. 10 (2021): 430.Status: Published
File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments
In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Information |
Volume | 12 |
Issue | 10 |
Pagination | 430 |
Date Published | 10/2021 |
Publisher | MDPI |
URL | https://www.mdpi.com/2078-2489/12/10/430 |
DOI | 10.3390/info12100430 |
Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images
Diagnostics 11, no. 12 (2021).Status: Published
Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images
Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies use a very small image resolution to save computing resources at the cost of losing details. Today, no conventions between resolution and performance exist, and monitoring the performance of various CNN architectures as a function of image resolution provides insights into how subtleties of different lesions on endoscopy affect performance. This can help set standards for image or video characteristics for future CNN-based models in gastrointestinal (GI) endoscopy. This study examines the performance of CNNs on the HyperKvasir dataset, consisting of 10,662 images from 23 different findings. We evaluate two CNN models for endoscopic image classification under quality distortions with image resolutions ranging from 32 × 32 to 512 × 512 pixels. The performance is evaluated using two-fold cross-validation and F1-score, maximum Matthews correlation coefficient (MCC), precision, and sensitivity as metrics. Increased performance was observed with higher image resolution for all findings in the dataset. MCC was achieved at image resolutions between 512 × 512 pixels for classification for the entire dataset after including all subclasses. The highest performance was observed with an MCC value of 0.9002 when the models were trained on the highest resolution and tested on the same resolution. Different resolutions and their effect on CNNs are explored. We show that image resolution has a clear influence on the performance which calls for standards in the field in the future.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Diagnostics |
Volume | 11 |
Issue | 12 |
Date Published | 09/2021 |
Publisher | MDPI |
URL | https://www.mdpi.com/2075-4418/11/12/2183 |
DOI | 10.3390/diagnostics11122183 |
Kvasir-Capsule, a video capsule endoscopy dataset
Scientific Data 8, no. 1 (2021): 142.Status: Published
Kvasir-Capsule, a video capsule endoscopy dataset
Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Scientific Data |
Volume | 8 |
Issue | 1 |
Pagination | 142 |
Publisher | Springer Nature |
URL | http://www.nature.com/articles/s41597-021-00920-z |
DOI | 10.1038/s41597-021-00920-z |
MedAI: Transparency in Medical Image Segmentation
Nordic Machine Intelligence 1, no. 1 (2021): 1-4.Status: Published
MedAI: Transparency in Medical Image Segmentation
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Nordic Machine Intelligence |
Volume | 1 |
Issue | 1 |
Pagination | 1 - 4 |
Date Published | Jan-11-2021 |
Publisher | NMI |
Place Published | Oslo |
URL | https://journals.uio.no/NMI/article/view/9140https://journals.uio.no/NMI... |
DOI | 10.5617/nmi.9140 |
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
IEEE Access 9 (2021): 40496-40510.Status: Published
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localization, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localization task. Likewise, the proposed ColonSegNetachieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimize miss-detection rates.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | IEEE Access |
Volume | 9 |
Pagination | 40496-40510 |
Publisher | IEEE |
Keywords | Medical image segmentation, ColonSegNet, colonoscopy, polyps, deep learning, detection, localization, benchmarking, Kvasir-SEG |
DOI | 10.1109/ACCESS.2021.3063716 |
SmartIO: Zero-overhead Device Sharing through PCIe Networking
ACM Transactions on Computer Systems 38, no. 1-2 (2021): 1-78.Status: Published
SmartIO: Zero-overhead Device Sharing through PCIe Networking
The large variety of compute-heavy and data-driven applications accelerate the need for a distributed I/O solution that enables cost-effective scaling of resources between networked hosts. For example, in a cluster system, different machines may have various devices available at different times, but moving workloads to remote units over the network is often costly and introduces large overheads compared to accessing local resources. To facilitate I/O disaggregation and device sharing among hosts connected using Peripheral Component Interconnect Express (PCIe) non-transparent bridges, we present SmartIO. NVMes, GPUs, network adapters, or any other standard PCIe device may be borrowed and accessed directly, as if they were local to the remote machines. We provide capabilities beyond existing disaggregation solutions by combining traditional I/O with distributed shared-memory functionality, allowing devices to become part of the same global address space as cluster applications. Software is entirely removed from the data path, and simultaneous sharing of a device among application processes running on remote hosts is enabled. Our experimental results show that I/O devices can be shared with remote hosts, achieving native PCIe performance. Thus, compared to existing device distribution mechanisms, SmartIO provides more efficient, low-cost resource sharing, increasing the overall system performance
Afilliation | Communication Systems, Machine Learning |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Department of Holistic Systems, Department of High Performance Computing |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | ACM Transactions on Computer Systems |
Volume | 38 |
Issue | 1-2 |
Number | 2 |
Pagination | 1–78 |
Date Published | 07/2021 |
Publisher | Association for Computing Machinery |
Place Published | New York, NY, United States |
ISSN | 0734-2071 |
URL | https://dl.acm.org/doi/10.1145/3462545 |
DOI | 10.1145/3462545 |
Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events
International Journal of Semantic Computing 15, no. 2 (2021): 161-187.Status: Published
Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events
Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | International Journal of Semantic Computing |
Volume | 15 |
Issue | 2 |
Number | 2 |
Pagination | 161 - 187 |
Date Published | Jan-06-2021 |
Publisher | World Scientific |
ISSN | 1793-351X |
Keywords | 3d CNN, classification, Detection, soccer events, spotting |
URL | https://www.worldscientific.com/doi/abs/10.1142/S1793351X2140002X |
DOI | 10.1142/S1793351X2140002X |
Book Chapter
Artificial Intelligence in Gastroenterology
In Artificial Intelligence in Medicine, 1-20. Cham: Springer International Publishing, 2021.Status: Published
Artificial Intelligence in Gastroenterology
The holy grail in endoscopy examinations has for a long time been assisted diagnosis using Artificial Intelligence (AI). Recent developments in computer hardware are now enabling technology to equip clinicians with promising tools for computer-assisted diagnosis (CAD) systems. However, creating viable models or architectures, training them, and assessing their ability to diagnose at a human level, are complicated tasks. This is currently an active area of research, and many promising methods have been proposed. In this chapter, we give an overview of the topic. This includes a description of current medical challenges followed by a description of the most commonly used methods in the field. We also present example results from research targeting some of these challenges, and a discussion on open issues and ongoing work is provided. Hopefully, this will inspire and enable readers to future develop CAD systems for gastroenterology.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Book Chapter |
Year of Publication | 2021 |
Book Title | Artificial Intelligence in Medicine |
Pagination | 1 - 20 |
Date Published | 09/2021 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-030-58080-3 |
Keywords | Anomaly detection, artificial intelligence, Gastrointestinal endoscopy, Hand-crafted features, Neural Networks, Performance, Semantic segmentation |
URL | https://link.springer.com/referenceworkentry/10.1007%2F978-3-030-58080-3... |
DOI | 10.1007/978-3-030-58080-3_163-2 |
Proceedings, refereed
Automated Clipping of Soccer Events using Machine Learning
In IEEE International Symphosium of Multimedia (ISM). IEEE, 2021.Status: Published
Automated Clipping of Soccer Events using Machine Learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | IEEE International Symphosium of Multimedia (ISM) |
Date Published | 12/2021 |
Publisher | IEEE |
DOI | 10.1109/ISM52913.2021.00042 |
Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images
In DDW 2021, 2021.Status: Published
Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | DDW 2021 |
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
In 25th International Conference on Pattern Recognition. Springer, 2021.Status: Published
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions), an automated, accurate, and fast segmentation can improve the quality of delineated lesion boundaries and reduce missed rate. The Endotect challenge provides an opportunity to benchmark computer vision methods by training on the publicly available Hyperkvasir and testing on a separate unseen dataset. In this paper, we propose a novel architecture called ``DDANet'' based on a dual decoder attention network. Our experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577, demonstrating the generalization ability of our model.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | 25th International Conference on Pattern Recognition |
Pagination | 307-314 |
Publisher | Springer |
Keywords | Benchmarking, Convolutional neural network, deep learning, Polyp segmentation |
DeepSynthBody: the beginning of the end for data deficiency in medicine
In The International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2021.Status: Published
DeepSynthBody: the beginning of the end for data deficiency in medicine
Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | The International Conference on Applied Artificial Intelligence (ICAPAI) |
Publisher | IEEE |
DOI | 10.1109/ICAPAI49758.2021.9462062 |
DivergentNets: Medical Image Segmentation by Network Ensemble
In EndoCV, 2021.Status: Accepted
DivergentNets: Medical Image Segmentation by Network Ensemble
Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classification. More recently, polyp segmentation has gained attention in the medical community. Segmentation has the advantage of being more accurate than per-frame classification or object detection as it can show the affected area in greater detail. For our contribution to the EndoCV 2021 segmentation challenge, we propose two separate approaches. First, a segmentation model named TriUNet composed of three separate UNet models. Second, we combine TriUNet with an ensemble of well-known segmentation models, namely UNet++, FPN, DeepLabv3, and DeepLabv3+, into a model called DivergentNets to produce more generalizable medical image segmentation masks. In addition, we propose a modified Dice loss that calculates loss only for a single class when performing multi-class segmentation, forcing the model to focus on what is most important. Overall, the proposed methods achieved the best average scores for each respective round in the challenge, with TriUNet being the winning model in Round I and DvergentNets being the winning model in Round II of the segmentation generalization challenge at EndoCV 2021
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | EndoCV |
Dutkat: A Multimedia System for Catching Illegal Catchers in a Privacy-Preserving Manner
In Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR'21). New York, NY, USA: ACM, 2021.Status: Published
Dutkat: A Multimedia System for Catching Illegal Catchers in a Privacy-Preserving Manner
Fish crime is considered a global and serious problem for a healthy and sustainable development of one of mankind's important sources of food. Technological surveillance and control solutions are emerging as remedies to combat criminal activities, but such solutions might also come with impractical and negative side-effects and challenges. In this paper, we present the concept and design of a surveillance system in lieu of current surveillance trends striking a delicate balance between privacy of legal actors while simultaneously capturing evidence-based footage, sensory data, and forensic proofs of illicit activities. Our proposed novel approach is to assist human operators in the 24/7 surveillance loop of remote professional fishing activities with a privacy-preserving Artificial Intelligence (AI) surveillance system operating in the same proximity as the activities being surveyed. The system will primarily be using video surveillance data, but also other sensor data captured on the fishing vessel. Additionally, the system correlates with other sources such as reports from other fish catches in the approximate area and time, etc. Only upon true positive flagging of specific potentially illicit activities by the locally executing AI algorithms, can forensic evidence be accessed from this physical edge, the fishing vessel. Besides a more privacy-preserving solution, our edge-based AI system also benefits from much less data that has to be transferred over unreliable, low-bandwidth satellite-based networks.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR'21) |
Pagination | 57-61 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450385299 |
URL | https://dl.acm.org/doi/proceedings/10.1145/3463944 |
DOI | 10.1145/346394410.1145/3463944.3469102 |
Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2021.Status: Published
Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
Minimally Invasive Surgery (MIS) is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have open potential for computer-assisted procedures. However, there exists challenges and requirement to improve detection and tracking of the position of the instruments during these surgical procedures. To this end, we evaluate and compare some popular deep learning methods that can potentially be explored for the automated segmentation of surgical instruments in laparoscopy, an important step towards tool tracking. Our experimental results demonstrate that the Dual decoder attention network (DDANet) produces a superior result compared to other recent deep learning methods. DDANet produces a dice coefficient of 0.8739 and mean intersection over union of 0.8183 for the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019 dataset, at a real-time speed of 101.36 frames per second which is critical for such procedures.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) |
Pagination | 1-4 |
Publisher | IEEE |
Keywords | Real-time segmentation, minimally invasive surgery, surgical instruments, laparoscopy, deep learning |
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
In 27th International Conference on Multimedia Modeling. Springer, 2021.Status: Published
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | 27th International Conference on Multimedia Modeling |
Pagination | 196-205 |
Publisher | Springer |
Keywords | Accelerometer, Activity recognition, Audio, dataset, Sensor fusion |
HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)
In Proceedings of the 12th ACM Multimedia Systems Conference (MMSys '21). ACM, 2021.Status: Published
HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)
Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirements that come with patient-related data. Consequentially, many algorithms and models get published on the same topic without a standard benchmark to measure against. Therefore, this paper presents HYPERAKTIV, a public dataset containing health, activity, and heart rate data from patients diagnosed with attention deficit hyperactivity disorder, better known as ADHD. The dataset consists of data collected from 51 patients with ADHD and 52 clinical controls. In addition to the activity and heart rate data, we also include a series of patient attributes such as their age, sex, and information about their mental state, as well as output data from a computerized neuropsychological test. Together with the presented dataset, we also provide baseline experiments using traditional machine learning algorithms to predict ADHD based on the included activity data. We hope that this dataset can be used as a starting point for computer scientists who want to contribute to the field of mental health, and as a common benchmark for future work in ADHD analysis.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Proceedings of the 12th ACM Multimedia Systems Conference (MMSys '21) |
Pagination | 314–319 |
Publisher | ACM |
URL | https://dl.acm.org/doi/10.1145/3458305.3478454 |
DOI | 10.1145/3458305.3478454 |
Identification of spermatozoa by unsupervised learning from video data
In 37th Virtual Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE). Oxford University Press, 2021.Status: Published
Identification of spermatozoa by unsupervised learning from video data
Identification of individual sperm is essential to assess a given sperm sample's motility behavior. Existing computer-aided systems need training data based on annotations by professionals, which is resource demanding. On the other hand, data analysed by unsupervised machine learning algorithms can improve supervised algorithms that are more stable for clinical applications. Therefore, unsupervised sperm identification can improve computer-aided sperm analysis systems predicting different aspects of sperm samples. Other possible applications are assessing kinematics and counting of spermatozoa.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | 37th Virtual Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE) |
Publisher | Oxford University Press |
Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy
In DDW 2021, 2021.Status: Published
Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy
Convolutional neural networks (CNNs) are increasingly used to improve and automate processes in gastroenterology, like the detection of polyps during a colonoscopy. An important input to these methods is images and videos. Up until now, no well-defined, common understanding or standard regarding the resolution of the images and video frames has been defined, and to reduce processing time and resource requirements, images are today almost always down-sampled. However, how such down-sampling and the image resolution influence the performance in context with medical data is unknown. In this work, we investigate how the resolution relates to the performance of convolutional neural networks. This can help set standards for image or video characteristics for future CNN based models in gastrointestinal endoscopy.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | DDW 2021 |
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
In 27th International Conference on Multimedia Modeling. Vol. LNCS, volume 12573. Springer, 2021.Status: Published
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the ``Kvasir Instrument'' dataset which consists of $590$ annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple instruments, while the best result for both methods was observed on all other types of images. Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements. Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | 27th International Conference on Multimedia Modeling |
Volume | LNCS, volume 12573 |
Pagination | 218-229 |
Publisher | Springer |
Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation
In Mediaeval Medico 2021. Mediaeval 2021, 2021.Status: Published
Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation
The Medico Multimedia Task focuses on providing multimedia researchers with the opportunity to contribute to different areas of medicine using multimedia data to solve several subtasks. This year, the task focuses on transparency within machine learning-based medical segmentation systems, where the use case is gastrointestinal endoscopy. In this paper, we motivate the organization of this task, describe the development and test dataset, and present the evaluation process used to assess the participants' submissions.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Mediaeval Medico 2021 |
Publisher | Mediaeval 2021 |
URL | https://2021.multimediaeval.com/ |
Multimodal Virtual Avatars for Investigative Interviews with Children
In Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR '21). New York, NY, USA: ACM, 2021.Status: Published
Multimodal Virtual Avatars for Investigative Interviews with Children
In this article, we present our ongoing work in the field of training police officers who conduct interviews with abused children. The objectives in this context are to protect vulnerable children from abuse, facilitate prosecution of offenders, and ensure that innocent adults are not accused of criminal acts. There is therefore a need for more data that can be used for improved interviewer training to equip police with the skills to conduct high-quality interviews. To support this important task, we propose to research a training program that utilizes different system components and multimodal data from the field of artificial intelligence such as chatbots, generation of visual content, text-to-speech, and speech-to-text. This program will be able to generate an almost unlimited amount of interview and also training data. The goal of combining all these different technologies and datatypes is to create an immersive and interactive child avatar that responds in a realistic way, to help to support the training of police interviewers, but can also produce synthetic data of interview situations that can be used to solve different problems in the same domain.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR '21) |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450385299 |
URL | https://dl.acm.org/doi/proceedings/10.1145/3463944 |
DOI | 10.1145/346394410.1145/3463944.3469269 |
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
In 34th IEEE CBMS International Symposium on Computer-Based Medical Systems. IEEE, 2021.Status: Published
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting model size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | 34th IEEE CBMS International Symposium on Computer-Based Medical Systems |
Publisher | IEEE |
Keywords | colonoscopy, deep learning, segmentation, tool segmentation, Video capsule endoscopy |
Njord: An out-in-the-wild real world fish vessel catch analysis dataset
In Arctic Frontiers. Tromsø, Norway: Arctic Frontiers, 2021.Status: Published
Njord: An out-in-the-wild real world fish vessel catch analysis dataset
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Arctic Frontiers |
Publisher | Arctic Frontiers |
Place Published | Tromsø, Norway |
PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation
In Proc. of 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART). Paris / Créteil, France: IEEE, 2021.Status: Published
PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation
Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of a disease can play a vital role in treatment and decision-making. Convolutional neural network (CNN) based encoder-decoder techniques have advanced the performance of automated medical image segmentation systems. Several such CNN-based methodologies utilize techniques such as spatial- and channel-wise attention to enhance performance. Another technique that has drawn attention in recent years is residual dense blocks (RDBs). The successive convolutional layers in densely connected blocks are capable of extracting diverse features with varied receptive fields and thus, enhancing performance. However, consecutive stacked convolutional operators may not necessarily generate features that facilitate the identification of the target structures. In this paper, we propose a progressive alternating attention network (PAANet). We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales. The GAM allows the following layers in the dense blocks to focus on the spatial locations relevant to the target region. Every alternatePAAD block inverts the GAM to generate a reverse attention map which guides ensuing layers to extract boundary and edge-related information, refining the segmentation process. Our experiments on three different biomedical image segmentation datasets exhibit that our PAANet achieves favorable performance when compared to other state-of-the-art methods.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Proc. of 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) |
Date Published | 12/2021 |
Publisher | IEEE |
Place Published | Paris / Créteil, France |
URL | https://ieeexplore.ieee.org/document/9677844 |
DOI | 10.1109/BioSMART54244.2021.9677844 |
Sustainable Commercial Fishing: Digital Inspectors to the Rescue
In Arctic Frontiers. Arctic Frontiers, 2021.Status: Published
Sustainable Commercial Fishing: Digital Inspectors to the Rescue
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Arctic Frontiers |
Publisher | Arctic Frontiers |
Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images
In Nordic Machine Intelligence. Vol. 1. Nordic Machine Intelligence, 2021.Status: Published
Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images
Colorectal cancer is one of the deadliest and most widespread types of cancer in the world. Colonoscopy is the procedure used to detect and diagnose polyps from the colon, but today's detection rate shows a significant error rate that affects diagnosis and treatment. An automatic image segmentation algorithm may help doctors to improve the detection rate of pathological polyps in the colon. Furthermore, segmenting endoscopic tools in images taken during colonoscopy may contribute towards robotic assisted surgery. In this study, we trained and validated both pre-trained and not pre-trained segmentation models on two different data sets, containing images of polyps and endoscopic tools. Finally, we applied the models on two separate test sets and the best polyp model got a dice score 0.857 and the test instrument model got a dice score 0.948. Moreover, we found that pre-training of the models increased the performance in segmenting polyps and endoscopic tools.
Afilliation | Scientific Computing |
Project(s) | No Simula project |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Nordic Machine Intelligence |
Volume | 1 |
Number of Volumes | 1 |
Edition | 1 |
Pagination | 32 - 34 |
Date Published | 11/2021 |
Publisher | Nordic Machine Intelligence |
ISSN Number | 2703-9196 |
Keywords | convolutional neural networks, Hyperkvasir, MedAI challenge, Polyp segmentation, Transfer Learning |
URL | https://journals.uio.no/NMI/article/view/9132 |
DOI | 10.5617/nmi.9132 |
Visual Sentiment Analysis: A Natural Disaster Use-case Task at MediaEval 2021
In MediaEval 2021. Ceurws, 2021.Status: Published
Visual Sentiment Analysis: A Natural Disaster Use-case Task at MediaEval 2021
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | MediaEval 2021 |
Publisher | Ceurws |
Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters
In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). Aveiro, Portugal: IEEE, 2021.Status: Published
Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type |
Proceedings, non-refereed
Improving generalizibilty in polyp segmentation using ensemble convolutional neural network
In 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV2021). Vol. 2886. CEUR Workshop Proceedings, 2021.Status: Published
Improving generalizibilty in polyp segmentation using ensemble convolutional neural network
Medical image segmentation is a crucial task in medical image analysis. Despite near expert-label performance with the application of the deep learning method in medical image segmentation, the generalization of such models in the clinical environment remains a significant challenge. Transfer learning from a large medical dataset from the same domain is a common technique to address generalizability. However, it is difficult to find a similar large medical dataset. To address generalizability in polyp segmentation, we have used an ensemble of four MultiResUNet architectures, each trained on the combination of the different centered datasets provided by the challenge organizers. Our method achieved a decent performance of 0.6172 ± 0.0778 for the multi-centered dataset. Our study shows that significant work needs to be done to develop a computer-aided diagnosis system to detect and localize polyp of the multi-center datasets, which is essential for improving the quality of the colonoscopy.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, non-refereed |
Year of Publication | 2021 |
Conference Name | 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV2021) |
Volume | 2886 |
Publisher | CEUR Workshop Proceedings |
Keywords | colonoscopy, Convolutional neural network, health informatics, Polyp segmentation |
Miscellaneous
MMSys'21 Grand Challenge on Detecting Cheapfakes
arXiv, 2021.Status: Published
MMSys'21 Grand Challenge on Detecting Cheapfakes
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Miscellaneous |
Year of Publication | 2021 |
Publisher | arXiv |
Proceedings, refereed
ACM Multimedia BioMedia 2020 Grand Challenge Overview
In Proceedings of the 28th ACM International Conference on Multimedia. New York, NY, USA: Association for Computing Machinery, 2020.Status: Published
ACM Multimedia BioMedia 2020 Grand Challenge Overview
The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year's challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year's challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization's standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people's lives.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | Proceedings of the 28th ACM International Conference on Multimedia |
Pagination | 4655–4658 |
Publisher | Association for Computing Machinery |
Place Published | New York, NY, USA |
ISBN Number | 9781450379885 |
Keywords | artificial intelligence, Machine learning, male fertility, semen analysis, spermatozoa |
URL | https://doi.org/10.1145/3394171.3416287 |
DOI | 10.1145/3394171.3416287 |
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
In CBMS 2020: International Symposium on Computer-Based Medical Systems. IEEE, 2020.Status: Published
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | CBMS 2020: International Symposium on Computer-Based Medical Systems |
Publisher | IEEE |
Notes | This paper was nominated for the best paper award at CBMS 2020. |
ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications
In European Conference on Information Retrieval. Cham: Springer International Publishing, 2020.Status: Published
ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications
This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | European Conference on Information Retrieval |
Pagination | 533 - 541 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-030-45441-8 |
ISSN Number | 0302-9743 |
URL | https://link.springer.com/chapter/10.1007/978-3-030-45442-5_69 |
DOI | 10.1007/978-3-030-45442-5_69 |
Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
In International Conference on Multimedia Modeling. Springer, 2020.Status: Published
Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Gastrointestinal pathologies are periodically screened, biopsied, and resected using surgical tools. Usually, the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development, amount, and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic, we have released the ``Kvasir-Instrument'' dataset, which consists of 590 annotated frames containing GI procedure tools such as snares, balloons, and biopsy forceps, etc. Besides the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple tools, while the best result for both methods was observed on all other types of images. Both qualitative and quantitative results show that the model performs reasonably good, but there is potential for further improvements. Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | International Conference on Multimedia Modeling |
Publisher | Springer |
Keywords | Benchmarking, Convolutional neural network, Gastrointestinal endoscopy, Tool segmentation |
URL | https://www.springerprofessional.de/en/kvasir-instrument-diagnostic-and-... |
Kvasir-SEG: A Segmented Polyp Dataset
In International Conference on Multimedia Modeling. Daejeon, Korea: Springer, 2020.Status: Published
Kvasir-SEG: A Segmented Polyp Dataset
Pixel-wise image segmentation is a highly demanding task in medical image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated and verified by an experienced gastroenterologist. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep learning based CNN approach. This work will be valuable for researchers to reproduce results and compare their methods in the future. By adding segmentation masks to the Kvasir dataset, which until today only consisted of framewise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy videos.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | International Conference on Multimedia Modeling |
Pagination | 451-462 |
Publisher | Springer |
Place Published | Daejeon, Korea |
Keywords | Kvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet Fuzzy c-mean clustering, Semantic segmentation |
LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
In The Joint International Conference PDCAT-PAAP 2020. Springer, 2020.Status: Published
LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud-hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in deep neural networks (DNN). The proposed LightLayers consists of LightDense andLightConv2D layer that are as efficient as regular Conv2D and Dense layers, but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting into lightweight DNNmodels that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CI-FAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | The Joint International Conference PDCAT-PAAP 2020 |
Publisher | Springer |
Keywords | CIFAR-10, Convolutional neural network, Deep Learning, Fashion MNIST, Lightweight model, MNIST, Weight decomposition |
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
In Medico MediaEval 2020. CEUR, 2020.Status: Published
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the overlooked polyps. Such detection systems can help low-cost design solutions and save doctors time, which they could for example use to perform more patient examinations. In this paper, we introduce the 2020 Medico challenge, provide some information on related work and the dataset, describe the task and evaluation metrics, and discuss the necessity of organizing the Medico challenge.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | Medico MediaEval 2020 |
Publisher | CEUR |
Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data
In CLEF2020. CEUR Workshop Proceedings, 2020.Status: Published
Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | CLEF2020 |
Publisher | CEUR Workshop Proceedings |
Overview of ImageCLEF lifelog 2020: lifelog moment retrieval and sport performance lifelog
In CLEF2020 . CEUR Workshop Proceedings, 2020.Status: Published
Overview of ImageCLEF lifelog 2020: lifelog moment retrieval and sport performance lifelog
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | CLEF2020 |
Publisher | CEUR Workshop Proceedings |
Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications
In CLEF 2020. Vol. 12260. Cham: Springer International Publishing, 2020.Status: Published
Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications
This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | CLEF 2020 |
Volume | 12260 |
Pagination | 311 - 341 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-030-58218-0 |
ISSN Number | 0302-9743 |
URL | https://doi.org/10.1007/978-3-030-58219-7_22 |
DOI | 10.1007/978-3-030-58219-710.1007/978-3-030-58219-7_22 |
PMData: a sports logging dataset
In Proceedings of the 11th ACM Multimedia Systems Conference. ACM, 2020.Status: Published
PMData: a sports logging dataset
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | Proceedings of the 11th ACM Multimedia Systems Conference |
Pagination | 231-236 |
Publisher | ACM |
PSYKOSE: A Motor Activity Database of Patients with Schizophrenia
In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). Rochester, MN, USA: IEEE, 2020.Status: Published
PSYKOSE: A Motor Activity Database of Patients with Schizophrenia
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) |
Publisher | IEEE |
Place Published | Rochester, MN, USA |
URL | https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9169740http... |
DOI | 10.1109/CBMS49503.202010.1109/CBMS49503.2020.00064 |
Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
In MediaEval 2020. CEUR, 2020.Status: Published
Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
Segmentation of findings in the gastrointestinal tract is a challenging but also an important task which is an important building stone for sufficient automatic decision support systems. In this work, we present our solution for the Medico 2020 task, which focused on the problem of colon polyp segmentation. We present our simple but efficient idea of using an augmentation method that uses grids in a pyramid-like manner (large to small) for segmentation. Our results show that the proposed methods work as indented and can also lead to comparable results when competing with other methods.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | MediaEval 2020 |
Publisher | CEUR |
Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks
In 2020 IEEE International Symposium on Multimedia (ISM). IEEE, 2020.Status: Published
Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks
In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks. The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions. We test the method on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | 2020 IEEE International Symposium on Multimedia (ISM) |
Publisher | IEEE |
DOI | 10.1109/ISM.2020.00030 |
Scalable Infrastructure for Efficient Real-Time Sports Analytics
In Companion Publication of the 2020 International Conference on Multimodal Interaction. New York, NY, USA: ACM, 2020.Status: Published
Scalable Infrastructure for Efficient Real-Time Sports Analytics
Recent technological advances are adapted in sports to improve performance, avoid injuries, and make advantageous decisions. In this paper, we describe our ongoing efforts to develop and deploy PMSys, our smartphone-based athlete monitoring and reporting system. We describe our first attempts to gain insight into some of the data we have collected. Experiences so far are promising, both on the technical side and for athlete performance development. Our initial application of artificial-intelligence methods for prediction is encouraging and indicative.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | Companion Publication of the 2020 International Conference on Multimodal Interaction |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450380027 |
Keywords | algorithmic analysis, artificial intelligence, Machine learning, privacy-preserving data collection, Sports performance logging |
URL | https://dl.acm.org/doi/proceedings/10.1145/3395035https://dl.acm.org/doi... |
DOI | 10.1145/339503510.1145/3395035.3425300 |
The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
In 25th International Conference on Pattern Recognition (ICPR). IEEE, 2020.Status: Published
The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
The EndoTect challenge at the International Conference on Pattern Recognition 2020 aims to motivate the development of algorithms that aid medical experts in finding anomalies that commonly occur in the gastrointestinal tract. Using HyperKvasir, a large dataset containing images taken from several endoscopies, the participants competed in three tasks. Each task focuses on a specific requirement for making it useful in a real-world medical scenario. The tasks are (i) high classification performance in terms of prediction accuracy, (ii) efficient classification measured by the number of images classified per second, and (iii) pixel-level segmentation of specific anomalies. Hopefully, this can motivate different computer science researchers to help benchmark a crucial component of a future computer-aided diagnosis system, which in turn, could potentially save human lives.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | 25th International Conference on Pattern Recognition (ICPR) |
Publisher | IEEE |
Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros
In The ACM Multimedia Systems Conference (MMSys). The ACM Multimedia Systems Conference (MMSys): ACM, 2020.Status: Published
Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros
Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | The ACM Multimedia Systems Conference (MMSys) |
Publisher | ACM |
Place Published | The ACM Multimedia Systems Conference (MMSys) |
URL | https://dl.acm.org/doi/10.1145/3339825.3394939 |
DOI | 10.1145/3339825.3394939 |
Vid2Pix - A Framework for Generating High-Quality Synthetic Videos
In 2020 IEEE International Symposium on Multimedia (ISM). IEEE, 2020.Status: Published
Vid2Pix - A Framework for Generating High-Quality Synthetic Videos
Data is arguably the most important resource today as it fuels the algorithms powering services we use every day. However, in fields like medicine, publicly available datasets are few, and labeling medical datasets require tedious efforts from trained specialists. Generated synthetic data can be to future successful healthcare clinical intelligence. Here, we present a GAN-based video generator demonstrating promising results.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | 2020 IEEE International Symposium on Multimedia (ISM) |
Publisher | IEEE |
Journal Article
An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
ACM Transactions on Computing for Healthcare 1 (2020): 1-29.Status: Published
An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
Precise and efficient automated identification of Gastrointestinal (GI) tract diseases can help doctors treat more patients and improve the rate of disease detection and identification. Currently, automatic analysis of diseases in the GI tract is a hot topic in both computer science and medical-related journals. Nevertheless, the evaluation of such an automatic analysis is often incomplete or simply wrong. Algorithms are often only tested on small and biased datasets, and cross-dataset evaluations are rarely performed. A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level. Towards this goal, we present comprehensive evaluations of five distinct machine learning models using Global Features and Deep Neural Networks that can classify 16 different key types of GI tract conditions, including pathological findings, anatomical landmarks, polyp removal conditions, and normal findings from images captured by common GI tract examination instruments. Inour evaluation, we introduce performance hexagons using six performance metrics such as recall, precision, specificity, accuracy, F1-score, and Matthews Correlation Coefficient to demonstrate how to determine the real capabilities of models rather than evaluatingthem shallowly. Furthermore, we perform cross-dataset evaluations using different datasets for training and testing. With these cross-dataset evaluations, we demonstrate the challenge of actually building a generalizable model that could be used across different hospitals. Our experiments clearly show that more sophisticated performance metrics and evaluation methods need to be applied to get reliable models rather than depending on evaluations of the splits of the same dataset, i.e., the performance metrics should always be interpreted together rather than relying on a single metric.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | ACM Transactions on Computing for Healthcare |
Volume | 1 |
Number | 3 |
Pagination | 1-29 |
Publisher | ACM |
Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge
Medical Image Analysis (2020).Status: Published
Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video im-ages have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | Medical Image Analysis |
Date Published | 11/2020 |
Publisher | Elsevier |
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Scientific Data 7, no. 1 (2020): 1-14.Status: Published
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | Scientific Data |
Volume | 7 |
Issue | 1 |
Pagination | 1-14 |
Date Published | 08/2020 |
Publisher | Springer Nature |
Keywords | dataset, GI, Machine learning |
URL | http://www.nature.com/articles/s41597-020-00622-y |
DOI | 10.1038/s41597-020-00622-y |