A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading

Abstract

Diabetic Retinopathy (DR) is one of the most common microvascular complications of diabetes. Diabetic Macular Edema (DME) is a concomitant symptom of DR. As the grade of lesion of DR and DME increase, the possibility of blindness can also increase significantly. In order to take the early interventions as soon as possible to reduce the likelihood of blindness, it is necessary to perform both DR and DME grading. We design a joint grading model based on multi-task learning and multi-branch networks (MaMNet) for DR and DME grading. The model mainly includes a multi-branch network (MbN), a feature fusion module, and a disease classification module. The MbN is formed by four branch structures, which can extract the low-level feature information of DME and DR in a targeted way; the feature fusion module is composed of a self-feature extraction module (SFEN), cross-feature extraction module (CFEN) and atrous spatial pyramid pooling module (ASPP). By combining various features collected from the aforementioned modules, the feature fusion module can provide more thorough discriminative features, which benefits the joint grading accuracy. The ISBI-2018-IDRiD challenge dataset is used to evaluate the performance of the proposed model. The experimental results show that based on the multi-task strategy the two grading tasks of DR and DME can provide each other with additional useful information. The joint accuracy of the model, the accuracy of DR and the accuracy of DME are 61.2%, 64.1% and 79.4% respectively.

Keywords

• DR
• DME
• joint grading
• multi-branch network
• multi-task learning