Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview

TL;DR

This paper reviews fundus image datasets for diabetic retinopathy, analyzing their usability, limitations, and challenges to improve deep learning-based diagnosis and management.

Contribution

It provides a comprehensive categorization and evaluation of existing datasets, highlighting gaps and offering recommendations for future dataset development.

Findings

Datasets vary in size, accessibility, and annotation quality.

Current datasets lack standardized lesion-level annotations.

Challenges include limited longitudinal data and geographic diversity.

Abstract

Diabetic Retinopathy (DR) is a serious microvascular complication of diabetes, and one of the leading causes of vision loss worldwide. Although automated detection and grading, with Deep Learning (DL), can reduce the burden on ophthalmologists, it is constrained by the limited availability of high-quality datasets. Existing repositories often remain geographically narrow, contain limited samples, and exhibit inconsistent annotations or variable image quality; thereby, restricting their clinical reliability. This paper presents a comprehensive review and comparative analysis of fundus image datasets used in the management of DR. The study evaluates their usability across key tasks, including binary classification, severity grading, lesion localization, and multi-disease screening. It also categorizes the datasets by size, accessibility, and annotation type (such as image-level, lesion-level, and multi-disease). Finally, a recently published dataset is presented as a case study to illustrate broader challenges in dataset curation and usage. The review consolidates current knowledge while highlighting persistent gaps such as the lack of standardized lesion-level annotations and longitudinal data. It also outlines recommendations for future dataset development to support clinically reliable and explainable solutions in DR screening.