Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging

TL;DR

This paper presents deep learning methods for automated analysis of ultra-widefield fundus images to detect diabetic retinopathy and macular edema, aiming to improve early diagnosis and clinical efficiency.

Contribution

It introduces novel deep learning models and strategies specifically tailored for UWF image analysis in the context of diabetic eye disease detection.

Findings

High accuracy in image quality assessment

Effective detection of referable diabetic retinopathy

Reliable identification of diabetic macular edema

Abstract

Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss. Early detection through ultra-widefield fundus imaging enhances patient outcomes but presents challenges in image quality and analysis scale. This paper introduces deep learning solutions for automated UWF image analysis within the framework of the MICCAI 2024 UWF4DR challenge. We detail methods and results across three tasks: image quality assessment, detection of referable DR, and identification of DME. Employing advanced convolutional neural network architectures such as EfficientNet and ResNet, along with preprocessing and augmentation strategies, our models demonstrate robust performance in these tasks. Results indicate that deep learning can significantly aid in the automated analysis of UWF images, potentially improving the efficiency and accuracy of DR and DME detection in clinical settings.