CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
Medical image anomaly detection is central to timely diagnosis and clinical decision support, yet abnormal samples are costly to collect because of disease rarity, privacy concerns, and expert workload. This motivates unsupervised learning from normal images, where abnormalities are detected as deviations from learned normal patterns. However, medical anomalies are often subtle, local, and intertwined with normal anatomical variations, which complicates reliable normality modeling. Distillation-based methods support normality modeling by using frozen pretrained teachers as stable feature references, yet mismatches between generic teacher priors and student representations adapted to medic...
| Authors | Zihan Nie, Muhao Xu, Wei Feng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 July 2026 |
| Last updated at source | 22 September 2026 |
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