Papers › VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers

VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers

14 Apr 2021arXiv:2104.06757archive 2025-07-28

Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod, Salah A. Baker

In Fluorescein Angiography (FA), an exogenous dye is injected in the bloodstream to image the vascular structure of the retina. The injected dye can cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even death. In contrast, color fundus imaging is a non-invasive technique used for photographing the retina but does not have sufficient fidelity for capturing its vascular structure. The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA). However, OCTA equipment is quite expensive, and stable imaging is limited to small areas on the retina. In this paper, we propose a novel conditional generative adversarial network (GAN) capable of simultaneously synthesizing FA images from fundus photographs while predicting retinal degeneration. The proposed system has the benefit of addressing the problem of imaging retinal vasculature in a non-invasive manner as well as predicting the existence of retinal abnormalities. We use a semi-supervised approach to train our GAN using multiple weighted losses on different modalities of data. Our experiments validate that the proposed architecture exceeds recent state-of-the-art generative networks for fundus-to-angiography synthesis. Moreover, our vision transformer-based discriminators generalize quite well on out-of-distribution data sets for retinal disease prediction.

PaperPDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

SharifAmit/VTGAN officialmentioned in papermentioned on GitHubtf report
Tinysqua/VTGAN-pytorch-version mentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Disease PredictionFundus to Angiography GenerationImage Generation

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Fundus to Angiography Generation Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients VTGAN FID 17.3 #1 of 9 Archive leaderboard report
Fundus to Angiography Generation Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients VTGAN Kernel Inception Distance 0.00053 #1 of 9 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AttentionDense ConnectionsFALayer NormalizationLinear LayerMulti-Head AttentionResidual ConnectionSoftmaxVision Transformer

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections