DeepDRiD: Diabetic Retinopathy—Grading and Image Quality Estimation Challenge

Ruhan Liu; Xiangning Wang; Qiang Wu; Ling Dai; Xi Fang; Tao Yan; Jaemin Son; Shiqi Tang; Jiang Li; Zijian Gao; Adrian Galdran; J.M. Poorneshwaran; Hao Liu; Jie Wang; Yerui Chen; Prasanna Porwal; Gavin Siew Wei Tan; Xiaokang Yang; Chao Dai; Haitao Song; Mingang Chen; Huating Li; Weiping Jia; Dinggang Shen; Bin Sheng; Ping Zhang

Patterns (Jun 2022)

DeepDRiD: Diabetic Retinopathy—Grading and Image Quality Estimation Challenge

Ruhan Liu,
Xiangning Wang,
Qiang Wu,
Ling Dai,
Xi Fang,
Tao Yan,
Jaemin Son,
Shiqi Tang,
Jiang Li,
Zijian Gao,
Adrian Galdran,
J.M. Poorneshwaran,
Hao Liu,
Jie Wang,
Yerui Chen,
Prasanna Porwal,
Gavin Siew Wei Tan,
Xiaokang Yang,
Chao Dai,
Haitao Song,
Mingang Chen,
Huating Li,
Weiping Jia,
Dinggang Shen,
Bin Sheng,
Ping Zhang

Affiliations

Ruhan Liu: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China; MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, China
Xiangning Wang: Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
Qiang Wu: Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
Ling Dai: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China; MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, China
Xi Fang: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
Tao Yan: Department of Electromechanical Engineering, University of Macau, Macao, China
Jaemin Son: VUNO Inc., Korea
Shiqi Tang: Department of Mathematics, City University of Hong Kong, Hong Kong, China
Jiang Li: Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Zijian Gao: School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China
Adrian Galdran: Bournemouth University, United Kingdom
J.M. Poorneshwaran: Healthcare Technology Innovation Centre, IIT Madras, India
Hao Liu: School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China
Jie Wang: School of Computer Science and Engineering, Beihang University, Beijing, China
Yerui Chen: Nanjing University of Science and Technology, Nanjing, China
Prasanna Porwal: Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
Gavin Siew Wei Tan: Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
Xiaokang Yang: MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, China
Chao Dai: Shanghai Zhi Tang Health Technology Co., LTD., China
Haitao Song: MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, China
Mingang Chen: Shanghai Key Laboratory of Computer Software Testing & Evaluating, Shanghai Development Center of Computer Software Technology, Shanghai, China
Huating Li: Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China; Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China; Corresponding author
Weiping Jia: Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China; Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
Dinggang Shen: School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Corresponding author
Bin Sheng: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China; MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, China; Corresponding author
Ping Zhang: Department of Computer Science and Engineering, The Ohio State University, Ohio, USA; Department of Biomedical Informatics, The Ohio State University, Ohio, USA; Translational Data Analytics Institute, The Ohio State University, Ohio, USA

Journal volume & issue: Vol. 3, no. 6
p. 100512

Abstract

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Summary: We described a challenge named “Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge” in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis. The bigger picture: Diabetic retinopathy (DR) is the most common disease caused by diabetes. Challenges are held to address real-world issues encountered in the design of DR automated screening systems to advance the technology in this area. Thus, we described a challenge named ''Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge'' in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI 2020) for fundus image assessment and DR grading. The scientific community responded positively to the challenge. In the challenge, we provided a deep DR image dataset (DeepDRiD) containing regular DR images and ultra-widefield (UWF) DR images, both having image quality and DR grading diagnosis. We discussed details of the three best algorithms in each sub-challenges. The results by the top algorithms showed that image quality assessment can be used as a target for further exploration.

DSML2: Proof-of-concept Data science output has been formulated, implemented, and tested for one domain/problem

Published in Patterns

ISSN: 2666-3899 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
Website: https://www.cell.com/patterns

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