Diabetic Retinopathy (DR) is the most prevalent cause of avoidable vision impairment, mainly affecting the working-age population in the world. Early diagnosis and timely treatment of diabetic retinopathy help in preventing blindness. This objective can be accomplished by a coordinated effort to organize large, regular screening programs. Particularly computer-assisted ones. Well-acquired, large, and diverse retinal image datasets are essential for developing and testing digital screening programs and the automated algorithms at their core. Therefore, we provide a large retinal image dataset, DeepDR (Deep Diabetic Retinopathy), to facilitate the following investigations in the community. First, unlike previous studies, in order to further promote the early diagnosis precision and robustness in practice, we provide the dual-view fundus images from the same eyes, e.g. the optic disc as the center and the fovea as the center, to classify and grade DR lesions. The expected results should outperform the state-of-the-art models built with the single-view fundus images. Second, we include various image quality of fundus images in DeepDR dataset to reflect the real scenario in practice. We expect to build a model that can estimate the image quality level to provide supportive guidance to the fundus image-based diagnosis. Lastly, to explore the extreme generalizability of a DR grading system, we desire to build a model that transfer the capability of DR diagnosis learned from a large number of regular fundus images to the ultra-widefield retinal images. Usually, we use the regular fundus images for initial screening; the widefield scanning performs as a further screening mean because it can provide complete eye information. To the best of our knowledge, our database, DeepDR (Deep Diabetic Retinopathy), is the largest database of DR patient population, and provide more than 1,000 patients data. In addition, it is the only dataset constituting dual-view fundus images from the same eyes and various distinguishable quality levels images. This data set provides information on the disease severity of diabetic retinopathy, and image quality level for each image.
Moreover, we provide the first ultra-widefield retinal image dataset to facilitate the study of model generalizability and meanwhile further extend the DR diagnose means from traditional fundus imaging to wide-field retinal photography. This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.
The challenge is subdivided into three tasks as follows (participants can submit results for at least one of the challenges):
● Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy using dual view retinal fundus images. For more details please refer to Sub-challenge 1.
● Image Quality Estimation: Fundus quality assessment for overall image quality, artifacts, clarity, and field definition. For more details please refer to Sub-challenge 2.
● Transfer Learning: Explore the generalizability of a Diabetic Retinopathy (DR) grading system. For more details please refer to Sub-challenge 3.
| Challenge Website Launched||October 25, 2019|
| Training Data Release (Images + Groundtruth)||January 22, 2020|
| Evaluation Data Release (Images only)||February 1, 2020|
| Site Open For Submissions||February 20, 2020|
| The leaderboard on Evaluation Data Release||
March 5, 2020
March 12, 2020
March 20, 2020
(allow three times submissions)
| Results and Paper Submission Deadline||March 13, 2020|
| Invitation to Participate at ISBI - 2020||March 16, 2020|
 Challenge workshop at ISBI 2020（On-site Competition ）
On the day of the challenge workshop
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