Papers › Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images...
Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks
Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod
Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no other fast and non-invasive technique exists that can generate FA without coupling with Fundus photography. To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus images. The proposed gan incorporates multiple attention based skip connections in generators and comprises novel residual blocks for both generators and discriminators. It utilizes reconstruction, feature-matching, and perceptual loss along with adversarial training to produces realistic Angiograms that is hard for experts to distinguish from real ones. Our experiments confirm that the proposed architecture surpasses recent state-of-the-art generative networks for fundus-to-angio translation task.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fundus to Angiography Generation | Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients | Attention2Angio w/o Perceptual Loss | FID | 20.7 | #2 of 9 | Archive leaderboard | report |
| Fundus to Angiography Generation | Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients | Attention2Angio w/o Perceptual Loss | Kernel Inception Distance | 0.00392 | #2 of 9 | Archive leaderboard | report |
| Fundus to Angiography Generation | Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients | Attention2Angio | FID | 24.6 | #4 of 9 | Archive leaderboard | report |
| Fundus to Angiography Generation | Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients | Attention2Angio | Kernel Inception Distance | 0.00087 | #4 of 9 | Archive leaderboard | report |
| Fundus to Angiography Generation | Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients | Attention2Angio w/o Perceptual Loss + Feature Matching Loss | FID | 47.5 | #8 of 9 | Archive leaderboard | report |
| Fundus to Angiography Generation | Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients | Attention2Angio w/o Perceptual Loss + Feature Matching Loss | Kernel Inception Distance | 0.00595 | #8 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
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