论文标题
内窥镜疾病检测挑战2020
Endoscopy disease detection challenge 2020
论文作者
论文摘要
尽管许多技术都是围绕内窥镜检查的，但需要从多个中心收集一个全面的数据集来解决大多数深度学习框架的概括问题。有什么比疾病检测和定位更重要的？通过我们广泛的临床和计算专家网络，我们收集了，策划和注释的胃肠道内窥镜视频帧。我们已经发布了此数据集，并发起了疾病检测和细分挑战EDD2020 https://edd2020.grand-challenge.org，以解决限制并探索新的方向。 EDD2020是一项人群采购计划，旨在测试最近深度学习方法的可行性并促进建立强大技术的研究。在本文中，我们概述了EDD2020数据集，挑战任务，评估策略以及测试数据结果的简短摘要。挑战研讨会后将起草一份详细的论文，并对结果进行更详细的分析。
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.
