# CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

arXiv: https://arxiv.org/abs/1911.01376 · HTML: https://mlsanity.com/paper/1911.01376/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 30 of 35)

- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision](https://mlsanity.com/paper/2308.07898/) (2023)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement](https://mlsanity.com/paper/2207.04183/) (2022)
- [Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images](https://mlsanity.com/paper/2110.01761/) (2021)
- [Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment](https://mlsanity.com/paper/2112.11700/) (2021)
- [RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation](https://mlsanity.com/paper/2201.11037/) (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)
- [DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysis](https://mlsanity.com/paper/2307.09787/) (2023)
- [Guided Context Gating: Learning to leverage salient lesions in retinal fundus images](https://mlsanity.com/paper/2406.13126/) (2024)
- [Confidence-aware multi-modality learning for eye disease screening](https://mlsanity.com/paper/2405.18167/) (2024)
- [Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification](https://mlsanity.com/paper/2207.01072/) (2022)
- [Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis](https://mlsanity.com/paper/2505.06271/) (2025)
- [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://mlsanity.com/paper/2107.00115/) (2021)
- [Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement](https://mlsanity.com/paper/2301.06943/) (2023)
- [Progressive Transfer Learning for Multi-Pass Fundus Image Restoration](https://mlsanity.com/paper/2504.10025/) (2025)
- [Integrating Non-Linear Radon Transformation for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.15883/) (2025)
- [Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning](https://mlsanity.com/paper/2507.19199/) (2025)
- [A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data](https://mlsanity.com/paper/2109.11806/) (2021)
- [Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detection](https://mlsanity.com/paper/2304.12719/) (2023)
- [PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis](https://mlsanity.com/paper/2403.19124/) (2024)
- [SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images](https://mlsanity.com/paper/2404.13386/) (2024)
- [Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning](https://mlsanity.com/paper/2404.13388/) (2024)
- [A better approach to diagnose retinal diseases: Combining our Segmentation-based Vascular Enhancement with deep learning features](https://mlsanity.com/paper/2405.16235/) (2024)
- [Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.19362/) (2025)
- [A Hybrid Framework Bridging CNN and ViT based on Theory of Evidence for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2510.26315/) (2025)
- [Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images](https://mlsanity.com/paper/2511.13353/) (2025)
- [TWLR: Text-Guided Weakly-Supervised Lesion Localization and Severity Regression for Explainable Diabetic Retinopathy Grading](https://mlsanity.com/paper/2512.13008/) (2025)

## Abstract (arXiv)

Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME. Moreover, the location information, e.g., macula and soft hard exhaust annotations, are widely used as a prior for grading. Such annotations are costly to obtain, hence it is desirable to develop automatic grading methods with only image-level supervision. In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision. Our key contributions include the disease-specific attention module to selectively learn useful features for individual diseases, and the disease-dependent attention module to further capture the internal relationship between the two diseases. We integrate these two attention modules in a deep network to produce disease-specific and disease-dependent features, and to maximize the overall performance jointly for grading DR and DME. We evaluate our network on two public benchmark datasets, i.e., ISBI 2018 IDRiD challenge dataset and Messidor dataset. Our method achieves the best result on the ISBI 2018 IDRiD challenge dataset and outperforms other methods on the Messidor dataset. Our code is publicly available at https://github.com/xmengli999/CANet.