Papers › f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks
Thomas Schlegl，PhilippSeeböck，Sebastian M.Waldstein，GeorgLangs，UrsulaSchmidt-Erfurth
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised learning yields good results if expert labeled training data is available, the visual variability, and thus the vocabulary of findings, we can detect and exploit, is limited to the annotated lesions. Here, we present fast AnoGAN (f-AnoGAN), a generative adversarial network (GAN) based unsupervised learning approach capable of identifying anomalous images and image segments, that can serve as imaging biomarker candidates. We build a generative model of healthy training data, and propose and evaluate a fast mapping technique of new data to the GAN’s latent space. The mapping is based on a trained encoder, and anomalies are detected via a combined anomaly score based on the building blocks of the trained model – comprising a discriminator feature residual error and an image reconstruction error. In the experiments on optical coherence tomography data, we compare the proposed method with alternative approaches, and provide comprehensive empirical evidence that f-AnoGAN outperforms alternative approaches and yields high anomaly detection accuracy. In addition, a visual Turing test with two retina experts showed that the generated images are indistinguishable from real normal retinal OCT images. The f-AnoGAN code is available at https://github.com/tSchlegl/f-AnoGAN.
Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Classification | GoodsAD | f-AnoGAN | AUPR | 66.6 | #9 of 11 | Archive leaderboard | report |
| Anomaly Classification | GoodsAD | f-AnoGAN | AUROC | 62.8 | #9 of 11 | Archive leaderboard | report |
| Anomaly Detection | Hyper-Kvasir Dataset | F-Anogan | AUC | 0.907 | #5 of 6 | Archive leaderboard | report |
| Anomaly Detection | LAG | F-anoGAN | AUC | 0.778 | #4 of 5 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | f-AnoGAN | Avg. Detection AUROC | 64.2 | #36 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | f-AnoGAN | Detection AUROC (only logical) | 65.8 | #36 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | f-AnoGAN | Detection AUROC (only structural) | 62.7 | #36 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | f-AnoGAN | Segmentation AU-sPRO (until FPR 5%) | 33.4 | #36 of 40 | 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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