用可学习原型增强医学图像异常检测，提升定位精度与可解释性。
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
- 可训练编码器结合注意力原型注入，引导重建过程。
- 新提出的对齐损失使原型分配更均衡，避免少数原型主导。
- 在多个数据集上优于现有方法，结果可视化清晰可解释。
医学图像异常检测因标注有限和域间差异面临挑战。现有基于重建的方法多依赖冻结的预训练编码器，难以适应特定领域特征且降低定位精度。原型学习虽具可解释性，但常出现原型坍塌，即少数原型主导训练过程。为此，我们提出 DNP-ConFormer 框架，融合可训练编码器、原型引导重建及多样性感知对齐损失。动量编码器实现稳定的域自适应表示学习，轻量级原型提取器通过注意力机制将信息丰富的正常原型注入解码器以指导重建。所提对齐目标促使特征与原型分配更均衡，有效缓解原型坍塌。在多个医学影像基准测试中，该方法显著提升表征质量与异常定位性能。可视化与原型分配分析进一步验证了方法的有效性与可解释性。代码已公开于 https://github.com/liluhu0/DNP-ConFormer。
原文摘要 · Abstract (English)
Anomaly detection in medical images is challenging due to limited annotations and the domain gap. Existing reconstruction-based methods often rely on frozen pre-trained encoders, restricting adaptation to domain-specific patterns and degrading localization accuracy. Meanwhile, prototype-based learning offers interpretable representations but commonly suffers from prototype collapse, where a few prototypes dominate training and reduce diversity. To address these issues, we propose DNP-ConFormer, a unified framework that integrates a trainable encoder with prototype-guided reconstruction and a Diversity-Aware Alignment Loss. A momentum encoder enables stable domain-adaptive representation learning, while a lightweight Prototype Extractor discovers informative normal prototypes and injects them into the decoder via attention to guide reconstruction. The proposed alignment objective further encourages balanced feature-to-prototype assignments, effectively mitigating prototype collapse. Extensive experiments on multiple medical imaging benchmarks demonstrate improved representation quality and anomaly localization compared with prior methods. Visualization and prototype assignment analyses further validate the effectiveness and interpretability of our approach. The code is available at https://github.com/liluhu0/DNP-ConFormer.
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