{"ID":6537512,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11509","arxiv_id":"2607.11509","title":"CFR-Net:Collaborative Feature Refnement Network for Medical Image Anomaly Detection","abstract":"Medical image anomaly detection remains challenging because networks pretrained on natural images often exhibit limited adaptability to medical images, where abnormal patterns appear as fine-grained local shifts, multi-scale contextual mismatches, and orientation-sensitive structural deviations. To address this, we propose the Collaborative Feature Refinement Network (CFR-Net), which combines shared teacher-student feature refinement before decoding with cross-space consistency after decoding. CFR-Net refines frozen teacher features and trainable student features using a Multi-Path Feature Refinement Module (MPFRM) with shared parameters, imposing common multi-path refinement rules on generic visual references and representations adapted to the medical domain, thereby mitigating domain discrepancy while modeling local, multi-scale, and orientation-sensitive feature characteristics. A variance-sensitive objective and dynamic ``homework set'' reorganization further support layer-adaptive consistency learning. Experiments on medical benchmarks show that CFR-Net achieves competitive anomaly classification and strong anomaly localization performance when trained on normal data.","short_abstract":"Medical image anomaly detection remains challenging because networks pretrained on natural images often exhibit limited adaptability to medical images, where abnormal patterns appear as fine-grained local shifts, multi-scale contextual mismatches, and orientation-sensitive structural deviations. To address this, we pro...","url_abs":"https://arxiv.org/abs/2607.11509","url_pdf":"https://arxiv.org/pdf/2607.11509v1","authors":"[\"Zihan Nie\",\"Muhao Xu\",\"Wei Feng\",\"Yuan Cui\",\"Hua Wei\",\"Sijie Niu\",\"Yi Wan\",\"Xunbin Wei\",\"Weiye Song\",\"Zongyuan Ge\"]","published":"2026-07-13T12:59:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}