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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 …