• 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. ...
    • 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 ...
    • 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 ...
    • 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 ...
    • HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy 

      Borgli, Hanna; Thambawita, Vajira; Smedsrud, Pia H; Hicks, Steven; Jha, Debesh; Eskeland, Sigrun Losada; Randel, Kristin Ranheim; Pogorelov, Konstantin; Lux, Mathias; Dang Nguyen, Duc Tien; Johansen, Dag; Griwodz, Carsten; Stensland, Håkon Kvale; Garcia-Ceja, Enrique; Schmidt, Peter T; Hammer, Hugo Lewi; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-28)
      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 ...
    • Kvasir-SEG: A Segmented Polyp Dataset 

      Jha, Debesh; Pia H, Smedsrud; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas; Johansen, Dag; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-01-24)
      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 by a medical doctor and then verified by an experienced ...
    • A multi-centre polyp detection and segmentation dataset for generalisability assessment 

      Ali, Sharib; Jha, Debesh; Ghatwary, Noha; Realdon, Stefano; Cannizzaro, Renato; Salem, Osama E.; Lamarque, Dominique; Daul, Christian; Riegler, Michael Alexander; Ånonsen, Kim Vidar; Petlund, Andreas; Halvorsen, Pål; Rittscher, Jens; de Lange, Thomas; East, James E (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-06)
      Polyps in the colon are widely known cancer precursors identifed by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason ...