用一张正常影像训练通用模型，实现跨病种跨模态异常检测
Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detection
- 基于预训练编码器与多轮迭代解码，从极少量正常样本中学习通用正常特征
- 仅用每数据集1张正常图像，在9个医学数据集上均达顶尖检测效果
- 适合临床场景中样本稀缺的跨领域异常检测任务
无监督医学异常检测受限于正常样本稀缺。现有方法通常为每个数据集或疾病训练专用模型，需每任务数百张正常图像，且缺乏跨模态泛化能力。我们提出语义迭代重建（SIR）框架，使单一通用模型可基于极少正常样本实现跨多样医学领域的异常检测。SIR利用预训练教师编码器提取多尺度深层特征，并采用紧凑的上-下结构解码器结合多轮迭代优化，强化深层特征空间中的正常性先验。该框架采用一次通用设计：通过混合来自九个异构数据集各一张正常样本进行训练，即可在对应测试集上实现有效异常检测，无需任务特定微调。在九个医学基准上的广泛实验表明，SIR在四种设置——单样本通用、全量通用、单样本专用、全量专用——下均达到当前最优表现，显著优于此前方法。SIR为多领域临床异常检测提供了高效可扩展的解决方案。
原文摘要 · Abstract (English)
Unsupervised medical anomaly detection is severely limited by the scarcity of normal training samples. Existing methods typically train dedicated models for each dataset or disease, requiring hundreds of normal images per task and lacking cross-modality generalization. We propose Semantic Iterative Reconstruction (SIR), a framework that enables a single universal model to detect anomalies across diverse medical domains using extremely few normal samples. SIR leverages a pretrained teacher encoder to extract multi-scale deep features and employs a compact up-then-down decoder with multi-loop iterative refinement to enforce robust normality priors in deep feature space. The framework adopts a one-shot universal design: a single model is trained by mixing exactly one normal sample from each of nine heterogeneous datasets, enabling effective anomaly detection on all corresponding test sets without task-specific retraining. Extensive experiments on nine medical benchmarks demonstrate that SIR achieves state-of-the-art under all four settings -- one-shot universal, full-shot universal, one-shot specialized, and full-shot specialized -- consistently outperforming previous methods. SIR offers an efficient and scalable solution for multi-domain clinical anomaly detection.
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