Kaggle.com is listing a deep-learning competition sponsored by the California Healthcare Foundation to achieve the best accuracy identifying diabetic retinopathy (DR) from retinal images provided by EyePACS, a free platform for retinopathy screening. The goal of this competition is to push an automated detection system to the limit of what is possible – ideally resulting in models with realistic clinical potential. The winning models will be open sourced to maximize the impact such a model can have on improving DR detection.
Submissions are scored based on the quadratic weighted kappa, which measures the agreement between two ratings. This metric typically varies from 0 (random agreement between raters) to 1 (complete agreement between raters). In the event that there is less agreement between the raters than expected by chance, this metric may go below 0. The quadratic weighted kappa is calculated between the scores assigned by the human rater and the predicted scores. Entrants must submit a csv file with the image name and a predicted DR level for each image. The order does not matter.
The total prize pool for this competition is $100,000, distributed as follows:
- 1st place – $50,000
- 2nd place – $30,000
- 3rd place – $20,000
Timeline
- July 20, 2015 – First Submission deadline. Each team must make its first submission by this deadline.
- July 20, 2015 – Team Merger deadline. This is the last day teams may merge with other teams
- July 27, 2015 – Final submission deadline
Clinicians can identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. While this approach is effective, its resource demands are high. The expertise and equipment required are often lacking in areas where the rate of diabetes in local populations is high and DR detection is most needed. As the number of individuals with diabetes continues to grow, the infrastructure needed to prevent blindness due to DR will become even more insufficient.
Why not try to make a difference?
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