Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models

TL;DR

This paper presents a training-free, zero-shot method for detecting anomalies in 3D brain MRI scans by leveraging 2D foundation models to create volumetric tokens, enabling effective and practical 3D anomaly detection without supervision.

Contribution

It introduces a novel framework that constructs 3D volumetric tokens from 2D slice features, allowing training-free, zero-shot anomaly detection in 3D MRI volumes.

Findings

Effective 3D anomaly detection without training

Compatible with standard GPU hardware

Outperforms slice-wise methods in volumetric detection

Abstract

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.