Multi-Label Ocular Disease Classification With A Dense Correlation Deep Neural Network

Early diagnosis and timely treatment of ocular diseases are vital to prevent irreversible vision loss. Color fundus photography is an effective and economic tool for fundus screening. Since few symptoms are visible in the early disease stages, automatic and robust diagnosing algorithms according to color fundus photographs are in urgent need. Existing studies concentrate on image-level diagnoses treating the eyes independently without utilizing the useful correlation information between the left and right eyes. Besides, they commonly target only one or several ocular disease categories at a time. Considering the importance of both patient-level diagnosis correlating bilateral eyes and multi-label disease classification, we propose a patient-level multi-label ocular disease classification model based on convolutional neural networks. Specifically, a dense correlation network (DCNet) is designed to tackle the problem. DCNet consists of three major modules, a backbone CNN for feature extraction, a spatial correlation module for feature correlation, and a classifier for classification score generation. The backbone CNN extracts two sets of features from the left and right color fundus photographs, respectively. Subsequently, the spatial correlation module captures the pixel-wise correlations between the two feature sets. Then, the processed features are fused to get a patient-level representation. The final disease classification is conducted with the patient-level representation. Adopting a multi-label soft margin loss, the effectiveness of the proposed model is evaluated on a publicly available dataset, and the classification performance is improved with a large margin compared with multiple baseline methods.