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

Tai Le Gia, Jaehyun Ahn

Keywords: Zero-shot Anomaly Detection, Anomaly Segmentation, Brain MRI

TL;DR: A fully training-free method that turns 2D foundation model features into compact 3D patch tokens, enabling zero-shot anomaly detection directly on full brain MRI volumes without prompts, labels, or fine-tuning.

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. The code used in this study is available at \url{https://github.com/DumBringer/CoDeGraph3D}.

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Detection and Diagnosis

Registration Requirement: Yes

Reproducibility: https://github.com/DumBringer/CoDeGraph3D

Visa & Travel: Yes

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

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Submission Number: 282