Paper Info
Title
DeepDRiD: Diabetic Retinopathy—Grading and Image Quality Estimation Challenge
Abstract
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.
Year
DOI
Venue
2022
10.1016/j.patter.2022.100512
Patterns
Keywords
DocType
Volume
diabetic retinopathy,screening,deep learning,artificial intelligence,challenge,retinal image,image quality analysis,ultra-widefield,fundus image
Journal
3
Issue
ISSN
Citations 
6
2666-3899
0
PageRank 
References 
Authors
0.34
2
26
Name
Order
Citations
PageRank
Ruhan Liu100.68
Xiangning Wang200.34
Qiang Wu3132.91
Ling Dai400.34
Xi Fang500.34
Tao Yan600.34
Jaemin Son7343.64
Shiqi Tang800.34
Jiang Li99210.67
Zijian Gao1000.34
Adrian Galdran1101.01
J M Poorneshwaran1200.34
Hao Liu1321259.74
Jie Wang1427153.08
Yerui Chen1500.34
Prasanna Porwal1600.34
Gavin S. Tan1761.48
Xiaokang Yang183581238.09
Chao Dai1900.34
Haitao Song2000.34
Mingang Chen2102.03
Huating Li22225.14
Weiping Jia23293.74
Dinggang Shen247837611.27
Bin Sheng2536861.19
Ping Zhang2602.37