REFUGE2 Challenge: Treasure for Multi-Domain Learning in Glaucoma Assessment

• URL: http://arxiv.org/abs/2202.08994v1
• Date: Fri, 18 Feb 2022 02:56:21 GMT
• Title: REFUGE2 Challenge: Treasure for Multi-Domain Learning in Glaucoma Assessment
• Authors: Huihui Fang, Fei Li, Huazhu Fu, Xu Sun, Xingxing Cao, Jaemin Son, Shuang Yu, Menglu Zhang, Chenglang Yuan, Cheng Bian, Baiying Lei, Benjian Zhao, Xinxing Xu, Shaohua Li, Francisco Fumero, Jose Sigut, Haidar Almubarak, Yakoub Bazi, Yuanhao Guo, Yating Zhou, Ujjwal Baid, Shubham Innani, Tianjiao Guo, Jie Yang, Jos\'e Ignacio Orlando, Hrvoje Bogunovi\'c, Xiulan Zhang, Yanwu Xu
• Abstract summary: REFUGE2 challenge released 2,000 color fundus images of four models, including Zeiss, Canon, Kowa and Topcon. Three sub-tasks were designed in the challenge, including glaucoma classification, cup/optic disc segmentation, and macular fovea localization. This article summarizes the methods of some of the finalists and analyzes their results.
• Score: 45.41988445653055
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Glaucoma is the second leading cause of blindness and is the leading cause of irreversible blindness disease in the world. Early screening for glaucoma in the population is significant. Color fundus photography is the most cost effective imaging modality to screen for ocular diseases. Deep learning network is often used in color fundus image analysis due to its powful feature extraction capability. However, the model training of deep learning method needs a large amount of data, and the distribution of data should be abundant for the robustness of model performance. To promote the research of deep learning in color fundus photography and help researchers further explore the clinical application signification of AI technology, we held a REFUGE2 challenge. This challenge released 2,000 color fundus images of four models, including Zeiss, Canon, Kowa and Topcon, which can validate the stabilization and generalization of algorithms on multi-domain. Moreover, three sub-tasks were designed in the challenge, including glaucoma classification, cup/optic disc segmentation, and macular fovea localization. These sub-tasks technically cover the three main problems of computer vision and clinicly cover the main researchs of glaucoma diagnosis. Over 1,300 international competitors joined the REFUGE2 challenge, 134 teams submitted more than 3,000 valid preliminary results, and 22 teams reached the final. This article summarizes the methods of some of the finalists and analyzes their results. In particular, we observed that the teams using domain adaptation strategies had high and robust performance on the dataset with multi-domain. This indicates that UDA and other multi-domain related researches will be the trend of deep learning field in the future, and our REFUGE2 datasets will play an important role in these researches.

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