Automatic Diagnosis of Glaucoma on Color Fundus Images Using Adaptive Mask Deep Network

Glaucoma, a disease characterized by the progressive and irreversible defect of the visual field, requires a lifelong course of treatment once it is confirmed, which highlights the importance of glaucoma early detection. Due to the diversity of glaucoma diagnostic indicators and the diagnostic uncertainty of ophthalmologists, deep learning has been applied to glaucoma diagnosis by automatically extracting characteristics from color fundus images, and that has achieved great performance recently. In this paper, we propose a novel adaptive mask deep network to obtain effective glaucoma diagnosis on retinal fundus images, which fully utilizes the prior knowledge of ophthalmologists on glaucoma diagnosis to synthesize attention masks of color fundus images to locate a reasonable region of interest. Based on the synthesized masks, our method could pay careful attention to the effective visual representation of glaucoma. Experiments on several public and private fundus datasets illustrate that our method could focus on the significant area of glaucoma diagnosis and simultaneously achieve great performance in both academic environments and practical medical applications, which provides a useful contribution to improve the automatic diagnosis of glaucoma.