# Towards Adversarial Retinal Image Synthesis

arXiv: https://arxiv.org/abs/1701.08974 · HTML: https://mlsanity.com/paper/1701.08974/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

## Built on by (most cited first, 13 of 13)

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- [ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations](https://mlsanity.com/paper/2101.00567/) (2021)
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- [Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling](https://mlsanity.com/paper/2412.12532/) (2024)
- [DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation](https://mlsanity.com/paper/2501.03466/) (2025)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image](https://mlsanity.com/paper/2301.11871/) (2022)
- [Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy](https://mlsanity.com/paper/2409.07422/) (2024)

## Abstract (arXiv)

Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality.