The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent diffusion models have shown promise in this task, accurately generating the complex structure of the human brain remains a challenge. In this paper, we propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy, using spatial context to guide and improve reconstruction. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.

翻译：使用监督式深度学习技术检测脑部MRI扫描中的病理变化可能具有挑战性，原因在于脑部解剖结构的多样性以及对标注数据集的需求。另一种方法是采用无监督异常检测，该方法仅需健康脑部的样本级标签来创建参考表征。随后可将该参考表征与不健康的脑部解剖结构进行逐像素比较，从而识别异常。为实现这一目标，需要生成式模型来生成解剖结构一致的健康脑部MRI扫描。尽管最近的扩散模型在此任务中展现出潜力，但准确生成人脑的复杂结构仍是一个挑战。本文提出一种方法，将扩散模型的生成任务重构为基于补丁的健康脑部解剖结构估计，利用空间上下文引导并改进重建效果。我们使用肿瘤和多发性硬化病变数据评估该方法，结果表明与现有基线相比相对提升了25.1%。