# GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy

arXiv: https://arxiv.org/abs/2007.09968 · HTML: https://mlsanity.com/paper/2007.09968/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains](https://mlsanity.com/paper/2307.04378/) (2023)
- [Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement](https://mlsanity.com/paper/2207.04183/) (2022)
- [Image Quality-aware Diagnosis via Meta-knowledge Co-embedding](https://mlsanity.com/paper/2303.15038/) (2023)
- [nnMobileNet: Rethinking CNN for Retinopathy Research](https://mlsanity.com/paper/2306.01289/) (2023)
- [Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations](https://mlsanity.com/paper/2406.06384/) (2024)
- [AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/2106.08732/) (2021)
- [Integrating Non-Linear Radon Transformation for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.15883/) (2025)
- [A New Perspective to Boost Vision Transformer for Medical Image Classification](https://mlsanity.com/paper/2301.00989/) (2023)
- [A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data](https://mlsanity.com/paper/2109.11806/) (2021)
- [RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images](https://mlsanity.com/paper/2403.15647/) (2024)
- [Semantic Data Augmentation Enhanced Invariant Risk Minimization for Medical Image Domain Generalization](https://mlsanity.com/paper/2502.05593/) (2025)
- [Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.19362/) (2025)
- [PARAGraph: Pathology-Anatomy-Aware Hierarchical Graph for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2608.08368/) (2026)

## Abstract (arXiv)

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.