用2D模型无训练实现3D脑MRI异常检测，保留立体结构信息。
Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models
- 通过多轴切片聚合生成3D局部块令牌，恢复立体空间上下文。
- 在4个脑部MRI数据集上达到0.91~0.96的AUC，无需微调或标注。
- 适合无标注数据、快速部署的临床异常筛查场景。
零样本异常检测（ZSAD）在医学影像中日益重要，可在无任务特定监督下识别异常，但现有方法多限于2D数据。将ZSAD扩展至3D医学图像极具挑战，因现有方法依赖切片特征或视觉-语言模型，无法捕捉体积结构。本文提出一种完全无需训练的3D脑部MRI ZSAD框架，通过2D基础模型处理多轴切片，聚合生成局部3D块令牌，重建立方体空间上下文，并直接接入基于距离的批量异常检测流程。该方法提供紧凑的3D表征，可在标准GPU上高效计算，无需微调、提示或监督。实验表明，该框架可有效将2D编码器的无训练批处理ZSAD拓展至完整3D MRI体积，为体积分割异常检测提供简单且鲁棒的新方案。
原文摘要 · Abstract (English)
Zero-shot anomaly detection (ZSAD) has gained increasing attention in medical imaging as a way to identify abnormalities without task-specific supervision, but most advances remain limited to 2D datasets. Extending ZSAD to 3D medical images has proven challenging, with existing methods relying on slice-wise features and vision-language models, which fail to capture volumetric structure. In this paper, we introduce a fully training-free framework for ZSAD in 3D brain MRI that constructs localized volumetric tokens by aggregating multi-axis slices processed by 2D foundation models. These 3D patch tokens restore cubic spatial context and integrate directly with distance-based, batch-level anomaly detection pipelines. The framework provides compact 3D representations that are practical to compute on standard GPUs and require no fine-tuning, prompts, or supervision. Our results show that training-free, batch-based ZSAD can be effectively extended from 2D encoders to full 3D MRI volumes, offering a simple and robust approach for volumetric anomaly detection.
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