Abstract
Applying machine learning to real-world medical data, e.g. from hospital archives, has the potential to revolutionize disease detection in brain images. However, detecting pathology in such heterogeneous cohorts is a difficult challenge. Normative modeling, a form of unsupervised anomaly detection, offers a promising approach to studying such cohorts where the ``normal'' behavior is modeled and can be used at subject level to detect deviations relating to disease pathology. Diffusion models have emerged as powerful tools for anomaly detection due to their ability to capture complex data distributions and generate high-quality images. Their performance relies on image restoration; differences between the original and restored images highlight potential abnormalities. However, unlike normative models, these diffusion model approaches do not incorporate clinical information which provides important context to guide the disease detection process. Furthermore, standard approaches often poorly restore healthy regions, resulting in poor reconstructions and suboptimal detection performance. We present CADD, the first conditional diffusion model for normative modeling in 3D images. To guide the healthy restoration process, we propose a novel inference inpainting strategy which balances anomaly removal with retention of subject-specific features. Evaluated on three challenging datasets, including clinical scans, which may have lower contrast, thicker slices, and motion artifacts, CADD achieves state-of-the-art performance in detecting neurological abnormalities in heterogeneous cohorts.
Abstract (translated)
将机器学习应用于实际的医疗数据（例如医院档案）有可能彻底改变脑部图像中的疾病检测方式。然而，在这种异质群体中检测病理是一项艰巨的任务。规范建模，一种无监督异常检测的形式，提供了一种有前景的方法来研究这些群体，其中“正常”的行为被建模，并可用于在个体层面检测与疾病病理相关的偏差。扩散模型由于其捕捉复杂数据分布和生成高质量图像的能力而成为异常检测的强大工具。它们的性能依赖于图像恢复；原始图像与恢复后的图像之间的差异突出了潜在的异常情况。然而，与规范模型不同的是，这些基于扩散的方法不包含临床信息，这为指导疾病检测过程提供了重要的背景。 此外，标准方法通常较差地恢复健康区域，导致重建质量低下和检测性能不佳。我们提出了CADD（条件性扩散模型），这是首个用于3D图像中规范建模的条件性扩散模型。为了引导健康的恢复过程，我们提出了一种新颖的推理修补策略，该策略平衡了异常移除与保留个体特征之间的关系。在三个具有挑战性的数据集上进行了评估，包括可能具有较低对比度、较厚切片和运动伪影的临床扫描，CADD在异质群体中检测神经学异常方面达到了最佳性能。
URL
https://arxiv.org/abs/2508.03594
