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IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge

Title
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge
Type
Article in International Scientific Journal
Year
2020
Authors
Prasanna Porwal
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Samiksha Pachade
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Manesh Kokare
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Girish Deshmukh
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Jaemin Son
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Woong Bae
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Lihong Liu
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Jianzong Wang
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Xinhui Liu
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Liangxin Gao
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TianBo Wu
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Jing Xiao
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Fengyan Wang
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Baocai Yin
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Yunzhi Wang
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Gopichandh Danala
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Linsheng He
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Yoon Ho Choi
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Yeong Chan Lee
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Sang-Hyuk Jung
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Zhongyu Li
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Xiaodan Sui
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Junyan Wu
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Xiaolong Li
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Ting Zhou
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Janos Totho
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Agnes Baran
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Xingzheng Lyu
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Li Cheng
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Qinhao Chu
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Pengcheng Li
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Xin Ji
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Sanyuan Zhang
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Yaxin Shen
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Bin Sheng
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Ruogu Fang
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Debdoot Sheet
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Andras Hajduo
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Yuanjie Zheng
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Ana Maria Mendonça
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Shaoting Zhang
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Aurélio Campilho
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Bin Zheng
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Dinggang Shen
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Luca Giancardo
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Gwenolé Quellec
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Fabrice Mériaudeau
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Journal
Vol. 59
Pages: 1-26
ISSN: 1361-8415
Publisher: Elsevier
Other information
Authenticus ID: P-00R-3ZZ
Abstract (EN): Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI-2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal subchallenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 26
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