Endoscopy disease detection challenge 2020
This addresses the need for robust disease detection in endoscopy for medical professionals, but it is incremental as it focuses on dataset creation and benchmarking rather than novel methods.
The authors tackled the problem of generalization in deep learning for endoscopy by creating and releasing a comprehensive, multi-center dataset of gastrointestinal endoscopy video frames, and they launched the EDD2020 challenge to test deep learning methods for disease detection and segmentation, with results summarized but detailed analysis pending.
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.