  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 in
disease progression in patients. To determine the disease severity levels,
ophthalmologists need to focus on the discriminative parts of the fundus
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 use an ordinal regression method to
obtain the soft labels. 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. Experimental results demonstrate the superior
performance of the proposed method on two public IDRiD and DeepDR datasets.
