Learning Discriminative Representations for Fine-Grained Diabetic Retinopathy Grading

Diabetic retinopathy is one of the leading causes of blindness. However, no specific symptoms of early DR lead to a delayed diagnosis, which results in disease progression in patients. To determine the disease severity levels, ophthalmologists need to focus on the discriminative parts of the retinal images. In recent years, deep learning has achieved great success in medical image analysis. However, most works directly employ algorithms based on convolutional neural networks (CNNs), which ignore the fact that the difference among classes is subtle and gradual. Hence, we consider automatic image grading of DR as a fine-grained classification task, and construct a bilinear model to identify the pathologically discriminative areas. In order to leverage the ordinal information among classes, we put the soft labels with ordinal information among classes into the loss function rather than the most commonly used one-hot labels for the diabetic retinopathy classification. In addition, other than only using a categorical loss to train our network, we also introduce the metric loss to learn a more discriminative feature space which is beneficial to locate the finer discriminative lesion parts. Experimental results demonstrate the superior performance of the proposed method on publicly available IDRiD, DeepDRiD and FGADR datasets.