用协同特征精炼提升医学图像异常检测精度
CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
- 设计共享教师-学生特征精炼模块，融合多路径特征优化
- 在正常数据上训练即实现优异的异常分类与定位性能
- 适合医学图像异常检测场景，尤其对细微结构偏差敏感
医学图像异常检测仍具挑战，因自然图像预训练网络在医学图像上适应性有限，异常表现为细粒度局部偏移、多尺度上下文不一致及方向敏感的结构偏差。为此，我们提出协同特征精炼网络（CFR-Net），结合解码前的共享师生特征精炼与解码后的跨空间一致性。CFR-Net通过含共享参数的多路径特征精炼模块（MPFRM），对冻结教师特征与可训练学生特征进行精炼，对通用视觉参考与医学领域适配表示施加共同多路径精炼规则，缓解领域差异的同时建模局部、多尺度和方向敏感特征。方差敏感目标与动态“作业集”重组织进一步支持层级自适应一致性学习。在多个医学基准测试中，CFR-Net仅需正常数据训练即实现竞争性异常分类性能与强异常定位能力。
原文摘要 · Abstract (English)
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.
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