LAUSR

Early computer-aided early diagnosis (CAD) based on retinal imaging is critical to the timely management and treatment planning of retina-related diseases. However, the inherent characteristics of retinal images and the complexity of their pathological patterns, such as low image contrast and different lesion sizes, restrict the performance of CAD systems. Recently, ultra-wide-field (UWF) retinal images have become a useful tool for disease detection due to the capability of capturing much broader view of retina (i.e., up to 200°), in comparison with the most commonly used retinal fundus images (45°). In this paper, we propose an attention-based multi-branch network for the diseases classification of four different subject groups. The proposed method consists of a multi-scale feature fusion module and a dual attention module. Specifically, small-scale lesions are identified using the features extracted from the multi-scale feature fusion module. To better explore the obtained features, the dual attention module with a global attention graph is incorporated to enable the network to recognize the salient objects of interest. Comprehensive validations on both private and public datasets were carried out to verify the effectiveness of the proposed model.