Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior

2026-09-21

AI Summary

本文提出了一种基于扩散先验的贝叶斯逆问题方法来实现无监督脑异常检测，通过联合推断伪健康图像和异常掩码以改善异常定位性能。

Abstract

Unsupervised anomaly detection (UAD) aims to localize abnormal regions in medical scans without pixel-level annotations. A typical strategy seeks to reconstruct a pseudo-healthy image that preserves subject-specific anatomy. Recently, diffusion models have been proposed to perform UAD. However, these methods rely on heuristic noise schedules or synthetic corruptions to balance subject-specificity and anomaly removal. In this work, we propose an alternative formulation of UAD as a Bayesian inverse problem under a diffusion prior. First, we introduce a latent spatial anomaly mask that models pixel-wise consistency between a test image and its latent corresponding pseudo-healthy image. Then, we propose an approximation of the unknown generation process that links healthy anatomy, anomalies, and the observed image, enabling a well-defined likelihood within the Bayesian framework. Building on recent advances in diffusion-based inverse problem methods, we jointly infer the pseudo-healthy image and the anomaly mask via annealed posterior sampling. We evaluate our approach on FDG PET (ADNI) and FLAIR MRI (BraTS 2021), demonstrating improved anomaly localization performance compared to other diffusion-based approaches and validating the contribution of our introduced model. Our code is available at https://github.com/HuguesRoy/UAD_DAPS.

Problem

• Unsupervised Anomaly Detection
• Medical Scans
• Diffusion Models
• Bayesian Inverse Problem

Innovation

• Bayesian inverse problem
• diffusion prior
• unsupervised anomaly detection
• latent spatial anomaly mask
• annealed posterior sampling