Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema

TL;DR

This study evaluates deep learning models on ultra-widefield imaging for diabetic retinopathy and macular edema detection, highlighting the effectiveness of vision transformers and feature fusion techniques.

Contribution

It benchmarks state-of-the-art DL models, including ViTs and foundation models, on UWF imaging for three clinical tasks, introducing a feature-level fusion approach.

Findings

ViTs and foundation models perform strongly across tasks.

Feature-level fusion enhances model robustness.

Frequency-domain representations improve UWF analysis.

Abstract

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness among working-age adults. Traditional approaches in the literature focus on standard color fundus photography (CFP) for the detection of these conditions. Nevertheless, recent ultra-widefield imaging (UWF) offers a significantly wider field of view in comparison to CFP. Motivated by this, the present study explores state-of-the-art deep learning (DL) methods and UWF imaging on three clinically relevant tasks: i) image quality assessment for UWF, ii) identification of referable diabetic retinopathy (RDR), and iii) identification of DME. Using the publicly available UWF4DR Challenge dataset, released as part of the MICCAI 2024 conference, we benchmark DL models in the spatial (RGB) and frequency domains, including popular convolutional neural networks (CNNs) as well as recent vision transformers (ViTs) and foundation models. In addition, we explore a final feature-level fusion to increase robustness. Finally, we also analyze the decisions of the DL models using Grad-CAM, increasing the explainability. Our proposal achieves consistently strong performance across all architectures, underscoring the competitiveness of emerging ViTs and foundation models and the promise of feature-level fusion and frequency-domain representations for UWF analysis.