Unsupervised anomaly detection model learns normative healthy retinal anatomy in OCT B-scans with discrete latent representations, layer-aware supervision and triplet learning, achieving AUROC 0.799 on Kermany and 0.884 cross-dataset on Srinivasan while providing pixel-level localization.
Spatial–contextual vari- ational autoencoder with attention correction for anomaly detection in retinal oct images.Computers in biology and medicine, 152:106328
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Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography
Unsupervised anomaly detection model learns normative healthy retinal anatomy in OCT B-scans with discrete latent representations, layer-aware supervision and triplet learning, achieving AUROC 0.799 on Kermany and 0.884 cross-dataset on Srinivasan while providing pixel-level localization.