# Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading

arXiv: https://arxiv.org/abs/2108.06763 · HTML: https://mlsanity.com/paper/2108.06763/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 15 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection](https://mlsanity.com/paper/1607.07155/) (2016)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection](https://mlsanity.com/paper/1706.04372/) (2017)
- [BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading](https://mlsanity.com/paper/1905.06312/) (2019)
- [Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading](https://mlsanity.com/paper/2010.15344/) (2020)

## Built on by (most cited first, 7 of 7)

- [Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images](https://mlsanity.com/paper/2211.14552/) (2022)
- [Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift](https://mlsanity.com/paper/2208.00465/) (2022)
- [Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling](https://mlsanity.com/paper/2205.10757/) (2022)
- [Residual Channel Attention Network for Brain Glioma Segmentation](https://mlsanity.com/paper/2205.10758/) (2022)
- [Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers](https://mlsanity.com/paper/2209.03761/) (2022)
- [Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data](https://mlsanity.com/paper/2304.06471/) (2023)
- [Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis](https://mlsanity.com/paper/2605.12929/) (2026)

## Abstract (arXiv)

Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients. However, vision loss occurs primarily in the late stages of DR, and the symptoms of visual impairment, ranging from mild to severe, can vary greatly, adding to the burden of diagnosis and treatment in clinical practice. Deep learning methods based on retinal images have achieved remarkable success in automatic DR grading, but most of them neglect that the presence of diabetes usually affects both eyes, and ophthalmologists usually compare both eyes concurrently for DR diagnosis, leaving correlations between left and right eyes unexploited. In this study, simulating the diagnostic process, we propose a two-stream binocular network to capture the subtle correlations between left and right eyes, in which, paired images of eyes are fed into two identical subnetworks separately during training. We design a contrastive grading loss to learn binocular correlation for five-class DR detection, which maximizes inter-class dissimilarity while minimizing the intra-class difference. Experimental results on the EyePACS dataset show the superiority of the proposed binocular model, outperforming monocular methods by a large margin.