# Synthesizing New Retinal Symptom Images by Multiple Generative Models

## TL;DR

This paper presents a novel approach combining GANs and style transfer to generate synthetic retinal images that aid in AMD diagnosis and feature understanding, outperforming traditional methods in classification tasks.

## Contribution

The study introduces an integrated pipeline for generating and verifying synthetic retinal images using GANs and style transfer, enhancing disease classification and feature discovery.

## Key findings

- Synthetic images contain detailed pathological features.
- Generated images improve AMD subtype classification accuracy.
- Synthetic images outperform original datasets in classification tasks.

## Abstract

Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision. There is limited access to high-quality relevant retinal images and poor understanding of the features defining sub-classes of this disease. Motivated by recent advances in machine learning we specifically explore the potential of generative modeling, using Generative Adversarial Networks (GANs) and style transferring, to facilitate clinical diagnosis and disease understanding by feature extraction. We design an analytic pipeline which first generates synthetic retinal images from clinical images; a subsequent verification step is applied. In the synthesizing step we merge GANs (DCGANs and WGANs architectures) and style transferring for the image generation, whereas the verified step controls the accuracy of the generated images. We find that the generated images contain sufficient pathological details to facilitate ophthalmologists' task of disease classification and in discovery of disease relevant features. In particular, our system predicts the drusen and geographic atrophy sub-classes of AMD. Furthermore, the performance using CFP images for GANs outperforms the classification based on using only the original clinical dataset. Our results are evaluated using existing classifier of retinal diseases and class activated maps, supporting the predictive power of the synthetic images and their utility for feature extraction. Our code examples are available online.