DME-Net: Diabetic Macular Edema Grading by Auxiliary Task Learning

Abstract

Diabetic macular edema (DME) is a consequence of diabetic retinopathy (DR), characterized by the abnormal accumulation of fluid and protein deposits in the macular region of the retina. Early detection and grading of DME is of great clinical significance, yet remains a challenging problem. In this work, we propose a highly accurate DME grading model by exploiting macular and hard exudate detection results in an auxiliary learning manner. Specifically, we adopt XGBoost [4] as the classifier, which allows us to use different types of multi-scale features that are extracted by the multi-scale feature extraction models from the image, hard exudate mask, macula mask, and macula image. Experiments have been conducted on the IDRiD and Messidor datasets. Our model achieves a large improvement over previous methods. Our method yields an accuracy of 0.9417 on IDRiD and beats the champion method of the “Diabetic Retinopathy: Segmentation and Grading Challenge” [1]. Our method also produces a high overall performance on Messidor, obtaining scores of 0.9591, 0.9712, 0.9824 and 0.9633 in terms of sensitivity, specificity, AUC and accuracy, respectively.