A database for publications published by researchers and students at SimulaMet.
Status
Research area
Proceedings, refereed
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 |
Journal articles
Dissecting the Performance of YouTube Video Streaming in Mobile Networks
International Journal on Network Management (2019).Status: Accepted
Dissecting the Performance of YouTube Video Streaming in Mobile Networks
| Afilliation | Communication Systems |
| Project(s) | MONROE: Measuring Mobile Broadband Networks in Europe , Department of Mobile Systems and Analytics |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | International Journal on Network Management |
| Publisher | Wiley |
Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles
IEEE Wireless Communications Magazine (2019).Status: Accepted
Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles
Recent advances in edge computing and caching have significant impacts on the developments of vehicular networks. Nevertheless, the heterogeneous requirements of onvehicle applications and the time-variability on popularity of contents, bring great challenges for edge servers to efficiently utilize their resources. Moreover, the high mobility of smart vehicles adds substantial complexity in jointly optimizing edge computing and caching. Artificial Intelligence (AI) can greatly enhance the cognition and intelligence of vehicular networks and thus assist in optimally allocating resources for the problems with diverse, time-variant, and complex features. In this article, we propose a new architecture that can dynamically orchestrate edge computing and caching resources to improve system utility by making full use of AI based algorithms. Then we formulate a joint edge computing and caching scheme to maximize system utility and develop a novel resource scheme by exploiting deep reinforcement learning. Numerical results demonstrate the effectiveness of the proposed scheme.
| Afilliation | Communication Systems |
| Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | IEEE Wireless Communications Magazine |
| Publisher | IEEE |
Automatic detection of passable roads after floods in remote sensed and social media data
Signal Processing: Image Communication (2019).Status: Accepted
Automatic detection of passable roads after floods in remote sensed and social media data
This paper addresses the problem of floods classification and floods aftermath detection based onboth social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.
| Afilliation | Communication Systems |
| Project(s) | UMOD: Understanding and Monitoring Digital Wildfires, Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Signal Processing: Image Communication |
| Publisher | Elsevier |
| Keywords | convolutional neural networks, Flood detection, Multimedia Indexing and Retrieval, Natural Disasters, Satellite Imagery, Social Media |
Co-designing Smart Lighting and Communication for Visible Light Networks
IEEE Transactions on Mobile Computing (TMC) (2019).Status: Accepted
Co-designing Smart Lighting and Communication for Visible Light Networks
| Afilliation | Communication Systems |
| Project(s) | Department of Mobile Systems and Analytics, Simula Metropolitan Center for Digital Engineering |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | IEEE Transactions on Mobile Computing (TMC) |
| Publisher | IEEE |
Deep Learning for Automatic Generation of Endoscopy Reports
Gastrointestinal Endoscopy 89, no. 6 (2019).Status: Accepted
Deep Learning for Automatic Generation of Endoscopy Reports
| Afilliation | Machine Learning |
| Project(s) | No Simula project, Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Gastrointestinal Endoscopy |
| Volume | 89 |
| Issue | 6 |
| Date Published | 06/2019 |
| Publisher | Elsevier |
| Place Published | Gastrointestinal Endoscopy |
| DOI | 10.1016/j.gie.2019.04.053 |
Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming
International Journal of Semantic Computing 13, no. 3 (2019).Status: Accepted
Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming
360 panorama video displayed through Virtual reality (VR) glasses or large screens o®ers immersive user experiences, but as such technology becomes commonplace, the need for e±cient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the di®erent trade-o®s in order to save bandwidth while preserving the video quality in the user's ̄eld-of-view (FoV). Using 360 VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top- box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection con ̄gurations. In this paper, we present an e±cient end-to-end design and real-world implementation of such a 360 streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-o® between bandwidth usage and the video quality in the user's FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-o®s through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | No Simula project, Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | International Journal of Semantic Computing |
| Volume | 13 |
| Issue | 3 |
| Number | 3 |
| Publisher | World Scientific |
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Nature Scientific Reports (2019).Status: Accepted
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data
are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology.
We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. The algorithms performed worse when participant data was added. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Nature Scientific Reports |
| Publisher | Nature Publishing Group |
Sequence effects in the estimation of software development effort
Journal of Systems and Software (2019).Status: Accepted
Sequence effects in the estimation of software development effort
Currently, little is known about how much the sequence in which software development tasks or projects are estimated affects judgment-based effort estimates. To gain more knowledge, we examined estimation sequence effects in two experiments. In the first experiment, 362 software professionals estimated the effort of three large tasks of similar sizes, whereas in the second experiment 104 software professionals estimated the effort of four large and five small tasks. The sequence of the tasks was randomised in both experiments. The first experiment, with tasks of similar size, showed a mean increase of 10% from the first to the second and a 3% increase from the second to the third estimate. The second experiment showed that estimating a larger task after a smaller one led to a mean decrease in the estimate of 24%, and that estimating a smaller task after a larger one led to a mean increase of 25%. There was no statistically significant reduction in the sequence effect with higher competence. We conclude that more awareness about how the estimation sequence affects the estimates may reduce potentially harmful estimation biases. In particular, it may reduce the likelihood of a bias towards too low effort estimates.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Journal of Systems and Software |
| Publisher | Elsevier |
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Nature Scientific Reports (2019).Status: Accepted
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data
are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology.
We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. The algorithms performed worse when participant data was added. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Nature Scientific Reports |
| Publisher | Springer Nature |
