• Artificial intelligence in dry eye disease 

      Storås, Andrea Marheim; Strumke, Inga; Riegler, Michael Alexander; Grauslund, Jakob; Hammer, Hugo Lewi; Yazidi, Anis; Halvorsen, Pål; Gundersen, Kjell Gunnar; Utheim, Tor Paaske; Jackson, Catherine Joan (Journal article; Tidsskriftartikkel, 2021-11-27)
      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. ...
    • Artificial intelligence in the fertility clinic: status, pitfalls and possibilities 

      Riegler, Michael Alexander; Stensen, Mette Haug; Witczak, Oliwia; Andersen, Jorunn Marie; Hicks, Steven; Hammer, Hugo Lewi; Delbarre, Erwan; Halvorsen, Pål; Yazidi, Anis; Holst, Nicolai; Haugen, Trine B. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-29)
      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 ...
    • Automatic thumbnail selection for soccer videos using machine learning 

      Husa, Andreas; Midoglu, Cise; Hammou, Malek; Hicks, Steven; Johansen, Dag; Kupka, Tomas; Riegler, Michael; Halvorsen, Pål (Chapter; Bokkapittel, 2022-08-05)
      Thumbnail selection is a very important aspect of online sport video presentation, as thumbnails capture the essence of important events, engage viewers, and make video clips attractive to watch. Traditional solutions in the soccer domain for presenting highlight clips of important events such as goals, substitutions, and cards rely on the manual or static selection of thumbnails. However, such ...
    • Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure 

      Storås, Andrea; Riegler, Michael Alexander; Haugen, Trine B.; Thambawita, Vajira L B; Hicks, Steven Alexander; Hammer, Hugo Lewi; Kakulavarapu, Radhika; Halvorsen, Pål; Stensen, Mette Haug (Chapter; Bokkapittel, 2023-02-02)
      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 ...
    • Áika: A Distributed Edge System for AI Inference 

      Alslie, Joakim Aalstad; Ovesen, Aril Bernhard; Nordmo, Tor-Arne Schmidt; Johansen, Håvard D.; Halvorsen, Pål; Riegler, Michael; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-17)
      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 ...
    • CELLULAR, A Cell Autophagy Imaging Dataset 

      al Outa, Amani; Hicks, Steven; Thambawita, Vajira L B; Andresen, Siri; Enserink, Jorrit Martijn; Halvorsen, Pål; Riegler, Michael Alexander; Knævelsrud, Helene (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-16)
      Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, ...
    • Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge 

      Ross, Tobias; Reinke, Annika; M. Full, Peter; Wagner, Martin; Kenngott, Hannes; Apitz, Martin; Hempe, Hellena; Mindroc Filimon, Diana; Scholz, Patrick; Tran, Thuy Nuong; Bruno, Pierangela; Arbeláez, Pablo; Bian, Gui-Bin; Bodenstedt, Sebastian; Lindström Bolmgren, Jon; Bravo-Sánchez, Laura; Chen, Hua-Bin; González, Cristina; Guo, Dong; Halvorsen, Pål; Heng, Pheng-Ann; Hosgor, Enes; Hou, Zeng-Guang; Isensee, Fabian; Jha, Debesh; Jiang, Tingting; Jin, Yueming; Kirtac, Kadir; Kletz, Sabrina; Leger, Stefan; Li, Zhixuan; H. Maier-Hein, Klaus; Ni, Zhen-Liang; Riegler, Michael; Schoeffmann, Klaus; Shi, Ruohua; Speidel, Stefanie; Stenzel, Michael; Twick, Isabell; Wang, Gutai; Wang, Jiacheng; Wang, Liansheng; Wang, Lu; Zhang, Yujie; Zhou, Yan-Jie; Zhu, Lei; Wiesenfarth, Manuel; Kopp-Schneider, Annette; P. Müller-Stich, Beat; Maier-Hein, Lena (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-28)
      Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images 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 ...
    • A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging 

      Jha, Debesh; Ali, Sharib; Hicks, Steven; Thambawita, Vajira L B; Borgli, Hanna; Smedsrud, Pia H.; de Lange, Thomas; Pogorelov, Konstantin; Wang, Xiaowei; Harzig, Philipp; Tran, Minh-Triet; Meng, Wenhua; Hoang, Trung-Hieu; Dias, Danielle; Ko, Tobey H.; Agrawal, Taruna; Ostroukhova, Olga; Khan, Zeshan; Tahir, Muhammed Atif; Liu, Yang; Chang, Yuan; Kirkerød, Mathias; Johansen, Dag; Lux, Mathias; Johansen, Håvard D.; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-19)
      Gastrointestinal (GI) 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. ...
    • A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation 

      Jha, Debesh; Smedsrud, Pia; Johansen, Dag; de Lange, Thomas; Johansen, Håvard D.; Halvorsen, Pål; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-01-05)
      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 ...
    • Deep learning and hand-crafted feature based approaches for polyp detection in medical videos 

      Pogorelov, Konstantin; Ostroukhova, Olga; Jeppsson, Mattis; Espeland, Håvard; Griwodz, Carsten; de Lange, Thomas; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-23)
      Video analysis including classification, segmentation or tagging is one of the most challenging but also interesting topics multimedia research currently try to tackle. This is often related to videos from surveillance cameras or social media. In the last years, also medical institutions produce more and more video and image content. Some areas of medical image analysis, like radiology or brain ...
    • Deep learning neural network can measure ECG intervals and amplitudes accurately 

      Kanters, Jørgen K.; Hicks, Steven; Isaksen, Jonas L; Grarup, Niels; Holstein-Rathlou, Niels-Henrik; Ghouse, Jonas; Ahlberg, Gustav; Olesen, Morten Salling; Linneberg, Allan; Ellervik, Christina; Hansen, Torben; Graff, Claus; Halvorsen, Pål; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-03)
    • DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine 

      Thambawita, Vajira; Isaksen, Jonas L.; Hicks, Steven A.; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Strümke, Inga; Hammer, Hugo L.; Maleckar, Mary M.; Halvorsen, Pål; Riegler, Michael A.; Kanters, Jørgen K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-09)
      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 ...
    • Dissecting deep neural networks for better medical image classification and classification understanding 

      Hicks, Steven Alexander; Riegler, Michael; Pogorelov, Konstantin; Ånonsen, Kim Vidar; de Lange, Thomas; Johansen, Dag; Jeppsson, Mattis; Randel, Kristin Ranheim; Eskeland, Sigrun Losada; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-23)
      Neural networks, in the context of deep learning, show much promise in becoming an important tool with the purpose assisting medical doctors in disease detection during patient examinations. However, the current state of deep learning is something of a "black box", making it very difficult to understand what internal processes lead to a given result. This is not only true for non-technical users but ...
    • DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation 

      Jha, Debesh; Riegler, Michael Alexander; Johansen, Dag; Halvorsen, Pål; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-01)
      Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net ...
    • Efficient disease detection in gastrointestinal videos – global features versus neural networks 

      Pogorelov, Konstantin; Riegler, Michael; Eskeland, Sigrun Losada; de Lange, Thomas; Johansen, Dag; Griwodz, Carsten; Schmidt, Peter Thelin; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-07-19)
      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 ...
    • Efficient live and on-demand tiled HEVC 360 VR video streaming 

      Jeppsson, Mattis; Espeland, Håvard; Kupka, Tomas; Langseth, Ragnar; Petlund, Andreas; Peng, Qiaoqiao; Xue, Chuansong; Johansen, Dag; Pogorelov, Konstantin; Stensland, Håkon Kvale; Griwodz, Carsten; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2019)
      360∘ panorama video displayed through Virtual Reality (VR) glasses or large screens offers immersive user experiences, but as such technology becomes commonplace, the need for efficient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360∘ panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light ...
    • Enhancing questioning skills through child avatar chatbot training with feedback 

      Røed, Ragnhild Klingenberg; Baugerud, Gunn Astrid; Zohaib Hassan, Syed; Shafiee Sabet, Saeed; Salehi, Pegah; Powell, Martine B.; Riegler, Michael Alexander; Halvorsen, Pål; Sinkerud Johnson, Miriam (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-13)
      Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to ...
    • Exploration of Different Time Series Models for Soccer Athlete Performance Prediction 

      Kulakou, Siarhei; Ragab, Nourhan; Midoglu, Cise; Boeker, Matthias; Johansen, Dag; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-29)
      Professional sports achievements combine not only the individual physical abilities of athletes but also many modern technologies in areas such as medicine, equipment production, nutrition, and physical and mental health monitoring. In this work, we address the problem of predicting soccer players’ ability to perform, from subjective self-reported wellness parameters collected using a commercially ...
    • FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation 

      Jha, Debesh; Riegler, Michael; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Halvorsen, Pål; Ali, Sharib (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-25)
      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 ...
    • File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments 

      Ovesen, Aril Bernhard; Nordmo, Tor-Arne Schmidt; Johansen, Håvard D.; Riegler, Michael Alexander; Halvorsen, Pål; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-18)
      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 ...