DTIL-Net: Dual-Task Interactive Learning Network for Automated Grading of Diabetic Retinopathy and Macular Edema

Jie Long, Yumei Tan, Shuxiang Song, Haiying Xia

Abstract: Diabetic retinopathy (DR) has been the leading cause of blindness associated with a common complication of diabetic macular oedema (DME). Automatic grading of diabetic retinopathy and diabetic macular oedema can reduce the risk of blindness. However, previous studies have focused only on grading DR or DME, often ignoring the interactive association between these two diseases. In this paper, we introduce the interactive learning network (DTIL-Net) for automated grading of DR and DME. DTIL-Net aims to explore and exploit the potential correlation between DR and DME. It consists of two main components: the attention module (AM) and the dual branch exchange module (DBEM). Specifically, we propose the Attention Module to create independent branches of lesion representations for these two diseases, thus enabling cross-channel interactions between high-level semantic features. Further, we introduce the Dual Branch Exchange Module (DBEM) to facilitate the exchange of lesion feature information between the two branches through feature squeezing and excitation operations, thus establishing an intrinsic link between DR and DME. In addition, considering the fine-grained nature of lesion features, we introduce a local diversity discrimination loss (LDD loss) to encourage the network to focus on more discriminative lesion regions. Extensive experiments on the Messidor-1 and IDRiD datasets show that DTIL-Net achieves superior results over existing state-of-the-art methods. On the Messidor-1 dataset, DR and DME classification outperform most other methods (DR: AUC: 97.0\(\%\), Acc: 93.0\(\%\); DME: AUC: 92.6\(\%\), Acc: 91.2\(\%\)).