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

TL;DR

This paper introduces a novel deep learning approach for diabetic retinopathy grading that enhances domain generalization through tailored augmentations, a new domain alignment loss, focal loss for imbalance, and self-supervised pretraining, significantly improving robustness.

Contribution

It presents a comprehensive domain generalization framework for DR grading, combining innovative augmentations, a domain alignment loss, focal loss, and self-supervised pretraining, which is novel in this context.

Findings

Significant improvement over baseline methods

Enhanced robustness to out-of-distribution data

Effective handling of label noise and data imbalance

Abstract

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.