ArticleBioengineering (Basel, Switzerland)2024
Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE).
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- An explainable unsupervised learning approach for anomaly detection on cornealFrontiers in bioengineering and biotechnology · 2025Article
- Precision measurement of stratum corneum thickness in OCT images using variational autoencoders and advanced DSP techniques.Frontiers in bioengineering and biotechnology · 2025Review
- A unified VQ-VAE framework for few-shot retinal vessel segmentation and multidisease classification.Digital healthArticle
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Authors and funding
3 authors.
Funding
Abstract
Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. First, a representation learning with a Vector-Quantized Variational Auto-Encoder (VQ-VAE) followed by Auto-Regressive (AR) modeling. Second, it exploits epistemic uncertainty estimates from Bayesian U-Net employed to segment the vasculature on OCTA en face images. Evaluation on two large public datasets, DRAC and OCTA-500, demonstrates effective anomaly detection (an AUROC of 0.92 for the DRAC and an AUROC of 0.75 for the OCTA-500) and localization (a mean Dice score of 0.61 for the DRAC) on this challenging task. To our knowledge, this is the first work that addresses anomaly detection in OCTA.
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Registered trials
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