用贝叶斯流网络检测阿尔茨海默病脑部影像异常，效果优于现有方法。
Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease
- 基于贝叶斯流网络构建新模型，支持带空间噪声的条件生成
- 在FDG PET数据上检测异常表现更准，假阳性率更低
- 适合神经影像异常检测研究者，尤其关注降低误报场景
无监督异常检测（UAD）在神经影像中至关重要，有助于识别与健康人群差异，从而辅助神经系统疾病诊断。本文首次将贝叶斯流网络（BFNs）这一新型生成模型应用于医学影像与异常检测。BFNs结合扩散框架与贝叶斯推断优势。我们提出AnoBFN，扩展自BFNs用于无监督异常检测，具备：一是在高空间相关噪声下进行条件图像生成；二是通过输入图像递归反馈机制保留个体特异性。在阿尔茨海默病相关的FDG PET影像异常检测任务中，AnoBFN优于基于变分自编码器（beta-VAE）、生成对抗网络（f-AnoGAN）及扩散模型（AnoDDPM）的先进方法，有效提升异常检出能力并降低假阳性率。
原文摘要 · Abstract (English)
Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological disorders. In this work, we focus on Bayesian flow networks (BFNs), a novel class of generative models, which have not yet been applied to medical imaging or anomaly detection. BFNs combine the strength of diffusion frameworks and Bayesian inference. We introduce AnoBFN, an extension of BFNs for UAD, designed to: i) perform conditional image generation under high levels of spatially correlated noise, and ii) preserve subject specificity by incorporating a recursive feedback from the input image throughout the generative process. We evaluate AnoBFN on the challenging task of Alzheimer's disease-related anomaly detection in FDG PET images. Our approach outperforms other state-of-the-art methods based on VAEs (beta-VAE), GANs (f-AnoGAN), and diffusion models (AnoDDPM), demonstrating its effectiveness at detecting anomalies while reducing false positive rates.
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