A Cross-Lesion Attention Network for Accurate Diabetic Retinopathy Grading With Fundus Images

Diabetic retinopathy (DR) is one of the major causes of blindness in the working-age population. Automatic DR grading with deep learning can help ophthalmologists treat patients in a timely manner. However, it is difficult to accurately grade DR because fundus images contain several different DR-related lesions such as soft exudates (SEs), hemorrhages (HEs), microaneurysms (MAs), and hard exudates (EXs), which are largely different in shape, appearance, and spatial location, and there exists strong multilesion dependency that can greatly affect the final grading results. In this article, a cross-lesion attention network (CLANet) is proposed, which can adaptively learn rich and discriminative imaging features from complicated lesions and model the dependencies of DR-related lesions. It consists of two main parts as follows. First, an adaptive lesion-aware (ALA) module based on adaptive channel-spatial convolution (ACSConv) can dynamically adjust convolution filters with different receptive fields to learn imaging features according to different DR lesions, so as to more flexibly and robustly handle the diversity of DR lesion features. Then, a cross-scale context attention (CSCA) module is developed to explore the dependencies of multiple DR-related lesions by sufficiently taking advantage of the long-range dependence learning ability of context network, which is further aggregated to learn multiscale context features in a coarse-to-fine manner. Experimental results on the public DR grading datasets [i.e., IDRiD, Messidor, and dataset for DR (DDR)] show that the proposed CLANet outperforms the state-of-the-art approaches.

Automated summary

In this study, a cross-lesion attention network (CLANet) is proposed for automatic diabetic retinopathy (DR) grading. CLANet can adaptively learn complex lesion imaging features and model the dependencies between DR-related lesions. Experimental results show that CLANet outperforms existing methods in DR grading.