基于条件扩散模型的脑部MRI异常检测无监督对比分析
Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
AI总结：
提出一种基于条件扩散模型的无监督对比分析框架，通过自监督对比编码器提取解剖特征来条件化扩散模型，显著提升了脑部MRI图像的重建质量和异常定位性能。
摘要：
对比分析（CA）通过将目标组（如不健康受试者）独有的模式与背景组（如健康受试者）的模式进行对比来检测异常。在脑部MRI领域，现有的CA方法依赖于监督对比学习或同时使用健康和不健康数据的变分自编码器（VAEs），但这种对目标样本的依赖在临床环境中具有挑战性。无监督异常检测（UAD）提供了一种替代方案，它通过学习健康解剖结构的参考表示而无需目标样本。偏离此参考分布即可指示潜在异常。在此背景下，由于扩散模型在图像生成方面优于VAEs，其在UAD中的应用日益增加。尽管如此，精确重建脑部解剖结构仍是一项挑战。在本研究中，我们提出了一个无监督框架，通过在健康图像上训练自监督对比编码器来提取有意义的解剖特征，从而提升重建质量。这些特征被用于条件化扩散模型，以重建给定图像的健康外观，从而通过逐像素比较实现可解释的异常定位。我们通过在面部图像数据集上的概念验证验证了该方法，并在四个脑部MRI数据集上进一步证明了其有效性，在NOVA基准上实现了最先进的异常定位性能。
英文摘要：
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). In the context of brain MRIs, existing CA approaches rely on supervised contrastive learning or variational autoencoders (VAEs) using both healthy and unhealthy data, but such reliance on target samples is challenging in clinical settings. Unsupervised Anomaly Detection (UAD) offers an alternative by learning a reference representation of healthy anatomy without the need for target samples. Deviations from this reference distribution can indicate potential anomalies. In this context, diffusion models have been increasingly adopted in UAD due to their superior performance in image generation compared to VAEs. Nonetheless, precisely reconstructing the anatomy of the brain remains a challenge. In this work, we propose an unsupervised framework to improve the reconstruction quality by training a self-supervised contrastive encoder on healthy images to extract meaningful anatomical features. These features are used to condition a diffusion model to reconstruct the healthy appearance of a given image, enabling interpretable anomaly localization via pixel-wise comparison. We validate our approach through a proof-of-concept on a facial image dataset and further demonstrate its effectiveness on four brain MRI datasets, achieving state-of-the-art anomaly localization performance on the NOVA benchmark.
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