Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions. Supervised machine learning methods are limited to a small number of pathologies where there is good availability of labeled data. In contrast, unsupervised anomaly detection (UAD) has the potential to identify a broader spectrum of anomalies by spotting deviations from normal patterns. Our research demonstrates that existing state-of-the-art UAD approaches do not generalise well to diverse types of anomalies in realistic multi-modal MR data. To overcome this, we introduce a new UAD method named Aggregated Normative Diffusion (ANDi). ANDi operates by aggregating differences between predicted denoising steps and ground truth backwards transitions in Denoising Diffusion Probabilistic Models (DDPMs) that have been trained on pyramidal Gaussian noise. We validate ANDi against three recent UAD baselines, and across three diverse brain MRI datasets. We show that ANDi, in some cases, substantially surpasses these baselines and shows increased robustness to varying types of anomalies. Particularly in detecting multiple sclerosis (MS) lesions, ANDi achieves improvements of up to 178% in terms of AUPRC.

翻译：医学图像（如脑部MRI）中异常的早期检测对于多种疾病的诊断与治疗具有重要意义。监督式机器学习方法仅限于标签数据充足的小部分病理类型。相比之下，无监督异常检测（UAD）通过识别与正常模式的偏差，有望检测更广泛的异常谱系。我们的研究表明，现有最先进的UAD方法在真实多模态MR数据中对多样化异常类型的泛化能力不足。为解决这一问题，我们提出一种名为聚合规范扩散（ANDi）的全新UAD方法。ANDi通过聚合经过金字塔高斯噪声训练的扩散概率模型（DDPMs）中预测去噪步骤与真实反向转移之间的差异来实现检测。我们在三个不同脑MRI数据集上将ANDi与三种近期UAD基线方法进行对比验证。实验表明，ANDi在某些情况下显著超越这些基线方法，并对多种异常类型表现出更强的鲁棒性。特别是在检测多发性硬化（MS）病灶时，ANDi在AUPRC指标上实现了最高达178%的提升。