• Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge 

      Ali, Sharib; Ghatwary, Noha; Jha, Debesh; Isik-Polat, Ece; Polat, Gorkem; Yang, Cheng; Li, Wuyang; Galdran, Adrian; Ballester, Miguel Angel Gonzalez; Thambawita, Vajira L B; Hicks, Steven; Poudel, Sahadev; Lee, Sang-Woong; Jin, Ziyi; Gan, Tianyuan; Yu, Chenghui; Yan, JiangPeng; Yeo, Doyeob; Lee, Hyunseok Lee; Tomar, Nikhil Kumar; Haitham, Mahmood; Ahmed, Amr; Riegler, Michael Alexander; Daul, Christian; Halvorsen, Pål; Rittscher, Jens; Salem, Osama E.; Lamarque, Dominique; Cannizzaro, Renato; Realdon, Stefano; de Lange, Thomas; East, James E (Peer reviewed; Journal article, 2024)
      Polyps are well‑known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal ...
    • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning 

      Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Riegler, Michael A.; Halvorsen, Pal (IEEE Access;Volume: 9, Peer reviewed; Journal article, 2021-03-04)
      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 ...
    • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning 

      Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Riegler, Michael; Halvorsen, Pål (IEEE Access;Volume: 9, 2021, Peer reviewed; Journal article, 2021-03-04)
      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 ...
    • Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges 

      Jha, Debesh; Sharma, Vanshali; Banik, Debapriya; Bhattacharya, Debayan; Roy, Kaushiki; Hicks, Steven; Tomar, Nikhil Kumar; Thambawita, Vajira L B; Krenzer, Adrian; Ji, Ge-Peng; Poudel, Sahadev; Batchkala, George; Alam, Saruar; Ahmed, Awadelrahman M.A.; Trinh, Quoc-Huy; Khan, Zeshan; Nguyen, Tien-Phat; Shrestha, Shruti; Nathan, Sabari; Gwak, Jeonghwan Gwak; Jha, Ritika Kumari; Zhang, Zheyuan; Schlaefer, Alexander; Bhattacharjee, Debotosh; Bhuyan, M.K.; Das, Pradip K.; Fan, Deng-Ping; Parasa, Sravanthi; Ali, Sharib; Riegler, Michael Alexander; Halvorsen, Pål; de Lange, Thomas; Bagci, Ulas (Peer reviewed; Journal article, 2024)
      Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging ...