Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
This addresses the challenge of accurate detection for diabetic patients, but it is incremental as it builds on existing image dehazing methods.
The paper tackled the problem of improving Diabetic Retinopathy detection accuracy by implementing a learned pre-processing step for shadow removal and color correction on eye fundus images, resulting in enhanced network performance.
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world. The main aim of this paper is to improve the accuracy of Diabetic Retinopathy detection by implementing a shadow removal and color correction step as a preprocessing stage from eye fundus images. For this, we rely on recent findings indicating that application of image dehazing on the inverted intensity domain amounts to illumination compensation. Inspired by this work, we propose a Shadow Removal Layer that allows us to learn the pre-processing function for a particular task. We show that learning the pre-processing function improves the performance of the network on the Diabetic Retinopathy detection task.