A database for publications published by researchers and students at SimulaMet.
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- All (542)
- Journal articles (103)
- Books (5)
- Proceedings, refereed (185) Remove Proceedings, refereed <span class="counter">(185)</span> filter
- Book chapters (4)
- Talks, keynote (14)
- PhD theses (2)
- Proceedings, non-refereed (16)
- Posters (6)
- Technical reports (14)
- Talks, invited (135)
- Talks, contributed (9)
- Public outreach (42)
- Miscellaneous (7)
Proceedings, refereed
Kvasir-SEG: A Segmented Polyp Dataset
In MMM2020. Daejeon, Korea: MMM 2020 26TH INTERNATIONAL CONFERENCE ON MULTIMEDIA MODELING, 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 | MMM2020 |
| Publisher | MMM 2020 26TH INTERNATIONAL CONFERENCE ON MULTIMEDIA MODELING |
| Place Published | Daejeon, Korea |
| Keywords | Kvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet Fuzzy c-mean clustering, Semantic segmentation |
A RASNet-based deep learning approach for the Binary Segmentation Task in the 2019 ROBUST-MIS Challenge
In Robust Medical Instrument Challenge at MICCAI 2019 (Part of Endoscopic Vision Challenge), 2020.Status: Accepted
A RASNet-based deep learning approach for the Binary Segmentation Task in the 2019 ROBUST-MIS Challenge
Semantic segmentation of laparoscopic instruments is one of the necessary pre-condition for computer and robotic assisted interventions. With the increase of publicly available datasets and improved hardware, the collaboration of multimedia and medical communities involved to tackle the surgical vision task is gaining momentum than ever before. In this regard, we present a solution for the 2019 Robust Medical Instrument Segmentation (ROBUST-MIS) challenge, which is a part of Endoscopic vision challenge. We use the RASNet model which is an encoder-decoder network. In the encoder part, RASNet uses the pre-trained ResNet50 model which is trained on ImageNet. The decoder part of the network consists of an attention fusion module along with a decoder block. Based on the results generated with the available dataset, we show that the proposed method produces good segmentation results on the instrument segmentation task. (*The paper is accepted for Joint Publication)
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | Robust Medical Instrument Challenge at MICCAI 2019 (Part of Endoscopic Vision Challenge) |
| Keywords | generalization, Instrument segmentation, minimally invasive surgery, Robustness |
Proceedings, refereed
Mobile Edge as Part of the Multi-Cloud Ecosystem: A Performance Study
In Proceedings of the 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). Pavia, Lombardia/Italy: IEEE Computer Society, 2019.Status: Published
Mobile Edge as Part of the Multi-Cloud Ecosystem: A Performance Study
Cloud computing has revolutionized the way of application usage and deployment: applications run cost-effectively in remote data centers. With the increasing need for mobility and micro-services, particularly with the upcoming 5G mobile broadband networks, there is also a strong demand for mobile edge computing (MEC): applications run in small cloud systems in close proximity to the user, in order to minimize latencies. Both cloud and MEC have their advantages and disadvantages. Combining the two approaches in a unified multi-cloud, consisting of both traditional cloud services provisioned over heterogeneous cloud platforms and MEC systems, has the potential of obtaining the best out of both worlds. However, a comprehensive study is needed to evaluate the performance gains and the overheads involved for real-world cloud applications. In this paper, we introduce a baseline performance evaluation in order to identify the fallacies and pitfalls of combining multiple cloud systems and MEC into a unified MEC-multi-cloud platform. For this purpose, we analyze the basic, application-independent performance metrics of average round-trip time (RTT) and average application payload throughput in a setup consisting of two private and one public cloud systems. This baseline performance analysis confirms the feasibility of MEC-multi-cloud, and provides guidelines for designing an autonomic resource provisioning solutions, in terms of an extension proposed to our existing Melodic middleware platform for multi-cloud applications.
| Afilliation | Communication Systems |
| Project(s) | MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing, NorNet, The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | Proceedings of the 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) |
| Pagination | 59-66 |
| Date Published | 02/2019 |
| Publisher | IEEE Computer Society |
| Place Published | Pavia, Lombardia/Italy |
| ISBN Number | 978-1-7281-1644-0 |
| Keywords | Cloud computing, latency, Mobile edge computing, Multi-Cloud, Performance |
| DOI | 10.1109/PDP.2019.00017 |
A Real-Time Video Streaming System over IPv6+MPTCP Technology
In Proceedings of the 1st International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing (M2EC) in conjunction with the 33rd International Conference on Advanced Information Networking and Applications (AINA). Matsue, Shimane/Japan: Springer, 2019.Status: Published
A Real-Time Video Streaming System over IPv6+MPTCP Technology
Today, a steadily increasing number of users not just passively consumes Internet content, but also creates and publishes content. Users publish text, photos and videos. With the availability of 5G high-speed, low-latency mobile broadband networks, also real-time video streaming will be possible. We believe this will become a very popular application in the coming years. But the more popular a service is, the higher the need for resilience. In this paper, we introduce our work-in-progress live video streaming platform for future mobile edge computing scenarios, which makes use of MPTCP+IPv6 to support multi-homing for resilience, and multi-path transport for load balancing. As a proof of concept, we show that the platform is (1) compatible with IPv6, (2) utilizes load balancing when possible, and (3) provides robustness by network redundancy.
| Afilliation | Communication Systems |
| Project(s) | NorNet, The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | Proceedings of the 1st International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing (M2EC) in conjunction with the 33rd International Conference on Advanced Information Networking and Applications (AINA) |
| Date Published | 03/2019 |
| Publisher | Springer |
| Place Published | Matsue, Shimane/Japan |
| Keywords | IPv6, Load Balancing, MPTCP, Resilience, Video Streaming Platform |
Measuring Web Quality of Experience in Cellular Networks
In Passive Active Measurements, 2019.Status: Accepted
Measuring Web Quality of Experience in Cellular Networks
| Afilliation | Communication Systems |
| Project(s) | MONROE: Measuring Mobile Broadband Networks in Europe , Department of Mobile Systems and Analytics |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | Passive Active Measurements |
Web Experience in Operational Mobile Networks: Lessons from Two Million Page Visits
In ACM Web Performance (www), 2019.Status: Accepted
Web Experience in Operational Mobile Networks: Lessons from Two Million Page Visits
| Afilliation | Communication Systems |
| Project(s) | MONROE: Measuring Mobile Broadband Networks in Europe , Department of Mobile Systems and Analytics |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | ACM Web Performance (www) |
Estimating Downlink Throughput from End-User Measurements in Mobile Broadband Networks
In IEEE World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, 2019.Status: Published
Estimating Downlink Throughput from End-User Measurements in Mobile Broadband Networks
| Afilliation | Communication Systems |
| Project(s) | MONROE: Measuring Mobile Broadband Networks in Europe |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | IEEE World of Wireless, Mobile and Multimedia Networks (WoWMoM) |
| Publisher | IEEE |
VISEM: A Multimodal Video Dataset of Human Spermatozoa
In 10th ACM Multimedia Systems Conference (MMSys), 2019.Status: Accepted
VISEM: A Multimodal Video Dataset of Human Spermatozoa
Real multimedia datasets that contain more than just images or text are rare. Even more so are open multimedia datasets in medicine. Often, clinically related datasets only consist of image or videos. In this paper, we present a dataset that is novel in two ways. Firstly, it is a multi-modal dataset containing different data sources such as videos, biological analysis data, and participant data. Secondly, it is the first dataset of that kind in the field of human reproduction. It consists of anonymized data from 85 different participants. We hope this dataset paper will inspire people to apply their knowledge in this important field, generate shareable results in the domain, and ultimately improve human infertility investigation and treatment.
| Afilliation | Machine Learning |
| Project(s) | No Simula project, Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 10th ACM Multimedia Systems Conference (MMSys) |
Unsupervised Preprocessing to Improve Generalisation for Medical Image Classification
In IEEE 13th International Symposium on Medical Information and Communication Technology (ISMICT). https://ieeexplore.ieee.org/document/8743979: IEEE, 2019.Status: Published
Unsupervised Preprocessing to Improve Generalisation for Medical Image Classification
Automated disease detection in videos and images from the gastrointestinal (GI) tract has received much attention in the last years. However, the quality of image data is often reduced due to overlays of text and positional data.
In this paper, we present different methods of preprocessing such images and we describe our approach to GI disease classification for the Kvasir v2 dataset.
We propose multiple approaches to inpaint problematic areas in the images to improve the anomaly classification, and we discuss the effect that such preprocessing does to the input data.
In short, our experiments show that the proposed methods improve the Matthews correlation coefficient by approximately 7% in terms of better classification of GI anomalies.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | IEEE 13th International Symposium on Medical Information and Communication Technology (ISMICT) |
| Publisher | IEEE |
| Place Published | https://ieeexplore.ieee.org/document/8743979 |
Summarizing E-Sports Matches and Tournaments: The Example of Counter-Strike: Global Offensive
In International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE). ACM, 2019.Status: Published
Summarizing E-Sports Matches and Tournaments: The Example of Counter-Strike: Global Offensive
That video and computer games have reached the masses is a well known fact. Furthermore, game streaming and watching other people play video games is another phenomenon that has outgrown its small beginning by far, and game streams, be it live or recorded, are today viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. However, as traditional sports, e-sports matches may be long and contain less interesting parts, introducing the challenge of producing well directed summaries and highlights. In this paper, we describe our efforts to approach the game streaming and e-sports phenomena from a multimedia research point of view. We focus on the challenge of summarizing matches from specific relevant game, Counter-Strike: Global Offensive (CS:GO). We survey related work, describe the rules and structure of the game and identify the main challenges for summarizing e-sports matches. With this contribution, we aim to foster multimedia research in the area of e-sports and game streaming.
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE) |
| Publisher | ACM |
