# Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview

arXiv: https://arxiv.org/abs/2604.02448 · HTML: https://mlsanity.com/paper/2604.02448/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 95 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision](https://mlsanity.com/paper/2308.07898/) (2023)
- [A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability](https://mlsanity.com/paper/2008.09772/) (2020)
- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Active label cleaning for improved dataset quality under resource constraints](https://mlsanity.com/paper/2109.00574/) (2021)
- [A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection](https://mlsanity.com/paper/2102.07978/) (2021)
- [Hallucination Index: An Image Quality Metric for Generative Reconstruction Models](https://mlsanity.com/paper/2407.12780/) (2024)
- [RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation](https://mlsanity.com/paper/2201.11037/) (2022)
- [Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations](https://mlsanity.com/paper/2406.06384/) (2024)
- [UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling](https://mlsanity.com/paper/2408.05618/) (2024)
- [Training a high-performance retinal foundation model with half-the-data and 400 times less compute](https://mlsanity.com/paper/2405.00117/) (2024)
- [MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy](https://mlsanity.com/paper/2402.04258/) (2024)
- [FunOTTA: On-the-Fly Adaptation on Cross-Domain Fundus Image via Stable Test-time Training](https://mlsanity.com/paper/2407.04396/) (2024)
- [Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images](https://mlsanity.com/paper/2304.11168/) (2023)
- [WDT-MD: Wavelet Diffusion Transformers for Microaneurysm Detection in Fundus Images](https://mlsanity.com/paper/2511.08987/) (2025)
- [Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye](https://mlsanity.com/paper/2501.12048/) (2025)

## Built on by (most cited first, 1 of 1)

- [An Event-Driven Framework for Fly-Inspired Visual Motion Detection](https://mlsanity.com/paper/2607.05205/) (2026)

## Abstract (arXiv)

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.