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.

翻译：零样本异常检测（ZSAD）作为一种无需任务特定监督即可识别异常的方法，在医学影像领域日益受到关注，但现有进展大多局限于二维数据集。将ZSAD扩展到三维医学影像已被证明具有挑战性，现有方法依赖于切片级特征和视觉-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三维脑部MRI零样本异常检测框架，该框架通过聚合经二维基础模型处理的多轴切片来构建局部化体积标记。这些三维补丁标记恢复了立方体空间上下文，并可直接与基于距离的批处理级异常检测流程集成。该框架提供了紧凑的三维表示，可在标准GPU上实际计算，且无需微调、提示或监督。我们的结果表明，基于批处理的免训练ZSAD能够有效地从二维编码器扩展到完整的三维MRI体积，为体积异常检测提供了一种简单而稳健的方法。