  The automatic grading of diabetic retinopathy (DR) facilitates medical
diagnosis for both patients and physicians. Existing researches formulate DR
grading as an image classification problem. As the stages/categories of DR
correlate with each other, the relationship between different classes cannot be
explicitly described via a one-hot label because it is empirically estimated by
different physicians with different outcomes. This class correlation limits
existing networks to achieve effective classification. In this paper, we
propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class
dependency prior into the original image classification network. The class
dependency prior is represented by a graph convolutional network with an
adjacency matrix. This prior augments image classification pipeline by
re-ranking classification results in a residual aggregation manner. Experiments
on the standard benchmarks have shown that GREEN performs favorably against
state-of-the-art approaches.
