# Transfer Learning for Retinal Vascular Disease Detection: A Pilot Study with Diabetic Retinopathy and Retinopathy of Prematurity

arXiv: https://arxiv.org/abs/2201.01250 · HTML: https://mlsanity.com/paper/2201.01250/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 4 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Supervised Transfer Learning at Scale for Medical Imaging](https://mlsanity.com/paper/2101.05913/) (2021)

## Built on by (most cited first, 5 of 5)

- [Risk of Transfer Learning and its Applications in Finance](https://mlsanity.com/paper/2311.03283/) (2023)
- [Feasibility and Transferability of Transfer Learning: A Mathematical Framework](https://mlsanity.com/paper/2301.11542/) (2023)
- [Feasibility of Transfer Learning: A Mathematical Framework](https://mlsanity.com/paper/2305.12985/) (2023)
- [Transfer Learning for Portfolio Optimization](https://mlsanity.com/paper/2307.13546/) (2023)
- [Sample Complexity of Transfer Learning: An Optimal Transport Approach](https://mlsanity.com/paper/2605.20545/) (2026)

## Abstract (arXiv)

Retinal vascular diseases affect the well-being of human body and sometimes provide vital signs of otherwise undetected bodily damage. Recently, deep learning techniques have been successfully applied for detection of diabetic retinopathy (DR). The main obstacle of applying deep learning techniques to detect most other retinal vascular diseases is the limited amount of data available. In this paper, we propose a transfer learning technique that aims to utilize the feature similarities for detecting retinal vascular diseases. We choose the well-studied DR detection as a source task and identify the early detection of retinopathy of prematurity (ROP) as the target task. Our experimental results demonstrate that our DR-pretrained approach dominates in all metrics the conventional ImageNet-pretrained transfer learning approach, currently adopted in medical image analysis. Moreover, our approach is more robust with respect to the stochasticity in the training process and with respect to reduced training samples. This study suggests the potential of our proposed transfer learning approach for a broad range of retinal vascular diseases or pathologies, where data is limited.