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Learning Pathology-Sensitive Representations for Unsupervised Retinal OCT Anomaly Detection
Deep learning approaches for retinal optical coherence tomography (OCT) predominantly rely on predefined disease categories, limiting their ability to detect rare or previously unseen abnormalities. While visual foundation models provide transferable representations, their effectiveness in identifyi
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