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Diabetic retinopathy (DR) is one of the leading causes of blindness. However,no specific symptoms of early DR lead to a delayed diagnosis, which results indisease progression in patients. To determine the disease severity levels,ophthalmologists need to focus on the discriminative parts of the fundusimages. In recent years, deep learning has achieved great success in medicalimage analysis. However, most works directly employ algorithms based onconvolutional neural networks (CNNs), which ignore the fact that the differenceamong classes is subtle and gradual. Hence, we consider automatic image gradingof DR as a fine-grained classification task, and construct a bilinear model toidentify the pathologically discriminative areas. In order to leverage theordinal information among classes, we use an ordinal regression method toobtain the soft labels. In addition, other than only using a categorical lossto train our network, we also introduce the metric loss to learn a morediscriminative feature space. Experimental results demonstrate the superiorperformance of the proposed method on two public IDRiD and DeepDR datasets.

Learning Discriminative Representations for Fine-Grained Diabetic Retinopathy Grading