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.
@article{arxiv.2007.09968,
title = {GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy},
author = {Shaoteng Liu and Lijun Gong and Kai Ma and Yefeng Zheng},
journal= {arXiv preprint arXiv:2007.09968},
year = {2020}
}