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.
Anomaly detection in optical coherence tomography angiog- raphy (octa) with a vector-quantized variational auto-encoder (vq-vae).Bioengineering, 11(7):682, 2024
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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.