Journal
MEDICAL IMAGE ANALYSIS
Volume 59, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.media.2019.101561
Keywords
Diabetic Retinopathy; Retinal image analysis; Deep learning; Challenge
Categories
Funding
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded (M.S.), INDIA [ISBI-2018]
- Research Grant for Intelligence Information Service Expansion Project [NIPA-0O202-17-1045]
- National Natural Science Foundation of China [61572300]
- National Key Research and Development Program of China [2016YFC1300302, 2017YFE0104000]
- Science and Technology Commission of Shanghai Municipality [16=501100, 17411952600]
- Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences [EFOP-3.6.2-16-2017-00015]
- European Union
- State of Hungary
- European Social Fund
- Beijing Shanggong Medical Technology Co., Ltd.
- A*STAR [A1818g0022]
- ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme
- National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) [CMUP-ERI/TIC/0028/2014]
- FCT [SFRH/BD/122365/2016]
Ask authors/readers for more resources
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. (C) 2019 Elsevier B.V. All rights reserved.
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