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Automatic analysis of colonoscopy images has been an active field of researchmotivated by the importance of early detection of precancerous polyps. However,detecting polyps during the live examination can be challenging due to variousfactors such as variation of skills and experience among the endoscopists, lackof attentiveness, and fatigue leading to a high polyp miss-rate. Deep learninghas emerged as a promising solution to this challenge as it can assistendoscopists in detecting and classifying overlooked polyps and abnormalitiesin real time. In addition to the algorithm's accuracy, transparency andinterpretability are crucial to explaining the whys and hows of the algorithm'sprediction. Further, most algorithms are developed in private data, closedsource, or proprietary software, and methods lack reproducibility. Therefore,to promote the development of efficient and transparent methods, we haveorganized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI:Transparency in Medical Image Segmentation (MedAI 2021)" competitions. Wepresent a comprehensive summary and analyze each contribution, highlight thestrength of the best-performing methods, and discuss the possibility ofclinical translations of such methods into the clinic. For the transparencytask, a multi-disciplinary team, including expert gastroenterologists, accessedeach submission and evaluated the team based on open-source practices, failurecase analysis, ablation studies, usability and understandability of evaluationsto gain a deeper understanding of the models' credibility for clinicaldeployment. Through the comprehensive analysis of the challenge, we not onlyhighlight the advancements in polyp and surgical instrument segmentation butalso encourage qualitative evaluation for building more transparent andunderstandable AI-based colonoscopy systems.

An objective validation of polyp and instrument segmentation methods in  colonoscopy through Medico 2020 polyp segmentation and MedAI 2021  transparency challenges