# From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening

arXiv: https://arxiv.org/abs/2511.11065 · HTML: https://mlsanity.com/paper/2511.11065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability](https://mlsanity.com/paper/2008.09772/) (2020)
- [Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy](https://mlsanity.com/paper/1710.01711/) (2017)
- [Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images](https://mlsanity.com/paper/2107.08274/) (2021)
- [Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement](https://mlsanity.com/paper/2207.04183/) (2022)
- [Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations](https://mlsanity.com/paper/2406.06384/) (2024)
- [Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy](https://mlsanity.com/paper/2308.09945/) (2023)
- [An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images](https://mlsanity.com/paper/2103.01702/) (2021)
- [Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models](https://mlsanity.com/paper/2208.11563/) (2022)
- [TJDR: A High-Quality Diabetic Retinopathy Pixel-Level Annotation Dataset](https://mlsanity.com/paper/2312.15389/) (2023)
- [Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions](https://mlsanity.com/paper/2411.00869/) (2024)
- [Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection](https://mlsanity.com/paper/2208.02408/) (2022)
- [Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning](https://mlsanity.com/paper/2507.19199/) (2025)
- [Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach](https://mlsanity.com/paper/2509.02918/) (2025)

## Abstract (arXiv)

Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with early detection critical for reducing vision loss worldwide. Over the past decade, deep learning has transformed DR screening, progressing from early convolutional neural networks trained on private datasets to advanced pipelines addressing class imbalance, label scarcity, domain shift, and interpretability. This survey provides the first systematic synthesis of DR research spanning 2016-2025, consolidating results from 50+ studies and over 20 datasets. We critically examine methodological advances, including self- and semi-supervised learning, domain generalization, federated training, and hybrid neuro-symbolic models, alongside evaluation protocols, reporting standards, and reproducibility challenges. Benchmark tables contextualize performance across datasets, while discussion highlights open gaps in multi-center validation and clinical trust. By linking technical progress with translational barriers, this work outlines a practical agenda for reproducible, privacy-preserving, and clinically deployable DR AI. Beyond DR, many of the surveyed innovations extend broadly to medical imaging at scale.