# Divergent Domains, Convergent Grading: Enhancing Generalization in Diabetic Retinopathy Grading

arXiv: https://arxiv.org/abs/2411.02614 · HTML: https://mlsanity.com/paper/2411.02614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 59 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 28 of 28)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Effective Data Augmentation With Diffusion Models](https://mlsanity.com/paper/2302.07944/) (2023)
- [Gradient Starvation: A Learning Proclivity in Neural Networks](https://mlsanity.com/paper/2011.09468/) (2020)
- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://mlsanity.com/paper/2103.10158/) (2021)
- [SelfReg: Self-supervised Contrastive Regularization for Domain Generalization](https://mlsanity.com/paper/2104.09841/) (2021)
- [Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization](https://mlsanity.com/paper/2109.02934/) (2021)
- [Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains](https://mlsanity.com/paper/2210.04133/) (2022)
- [How Useful is Self-Supervised Pretraining for Visual Tasks?](https://mlsanity.com/paper/2003.14323/) (2020)
- [Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels](https://mlsanity.com/paper/2103.13646/) (2021)
- [A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability](https://mlsanity.com/paper/2008.09772/) (2020)
- [Learn to Segment Retinal Lesions and Beyond](https://mlsanity.com/paper/1912.11619/) (2019)
- [G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection](https://mlsanity.com/paper/2006.09158/) (2020)
- [Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains](https://mlsanity.com/paper/2307.04378/) (2023)
- [Medical diffusion on a budget: Textual Inversion for medical image generation](https://mlsanity.com/paper/2303.13430/) (2023)
- [Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN](https://mlsanity.com/paper/2401.05159/) (2024)
- [Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining](https://mlsanity.com/paper/2308.04551/) (2023)
- [Generalizing Across Domains in Diabetic Retinopathy via Variational Autoencoders](https://mlsanity.com/paper/2309.11301/) (2023)
- [Generalizing to Unseen Domains in Diabetic Retinopathy Classification](https://mlsanity.com/paper/2310.17255/) (2023)
- [Domain-aware Triplet loss in Domain Generalization](https://mlsanity.com/paper/2303.01233/) (2023)
- [CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading](https://mlsanity.com/paper/2309.15493/) (2023)
- [DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification](https://mlsanity.com/paper/2309.09670/) (2023)

## Abstract (arXiv)

Diabetic Retinopathy (DR) constitutes 5% of global blindness cases. While numerous deep learning approaches have sought to enhance traditional DR grading methods, they often falter when confronted with new out-of-distribution data thereby impeding their widespread application. In this study, we introduce a novel deep learning method for achieving domain generalization (DG) in DR grading and make the following contributions. First, we propose a new way of generating image-to-image diagnostically relevant fundus augmentations conditioned on the grade of the original fundus image. These augmentations are tailored to emulate the types of shifts in DR datasets thus increase the model's robustness. Second, we address the limitations of the standard classification loss in DG for DR fundus datasets by proposing a new DG-specific loss, domain alignment loss; which ensures that the feature vectors from all domains corresponding to the same class converge onto the same manifold for better domain generalization. Third, we tackle the coupled problem of data imbalance across DR domains and classes by proposing to employ Focal loss which seamlessly integrates with our new alignment loss. Fourth, due to inevitable observer variability in DR diagnosis that induces label noise, we propose leveraging self-supervised pretraining. This approach ensures that our DG model remains robust against early susceptibility to label noise, even when only a limited dataset of non-DR fundus images is available for pretraining. Our method demonstrates significant improvements over the strong Empirical Risk Minimization baseline and other recently proposed state-of-the-art DG methods for DR grading. Code is available at https://github.com/sharonchokuwa/dg-adr.