Endoscopy disease detection challenge 2020

• URL: http://arxiv.org/abs/2003.03376v1
• Date: Sat, 7 Mar 2020 00:41:28 GMT
• Title: Endoscopy disease detection challenge 2020
• Authors: Sharib Ali, Noha Ghatwary, Barbara Braden, Dominique Lamarque, Adam Bailey, Stefano Realdon, Renato Cannizzaro, Jens Rittscher, Christian Daul, James East
• Abstract summary: This paper provides an overview of the EDD2020 dataset, challenge tasks, evaluation strategies and a short summary of results on test data. EDD2020 is a crowd sourcing initiative to test the feasibility of recent deep learning methods. A detailed paper will be drafted after the challenge workshop with more detailed analysis of the results.
• Score: 0.40631409309544825
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Whilst many technologies are built around endoscopy, there is a need to have a comprehensive dataset collected from multiple centers to address the generalization issues with most deep learning frameworks. What could be more important than disease detection and localization? Through our extensive network of clinical and computational experts, we have collected, curated and annotated gastrointestinal endoscopy video frames. We have released this dataset and have launched disease detection and segmentation challenge EDD2020 https://edd2020.grand-challenge.org to address the limitations and explore new directions. EDD2020 is a crowd sourcing initiative to test the feasibility of recent deep learning methods and to promote research for building robust technologies. In this paper, we provide an overview of the EDD2020 dataset, challenge tasks, evaluation strategies and a short summary of results on test data. A detailed paper will be drafted after the challenge workshop with more detailed analysis of the results.

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