使用聚合规范扩散的无监督异常检测
Unsupervised Anomaly Detection using Aggregated Normative Diffusion
AI总结：
本文提出无监督异常检测方法ANDi，通过在金字塔高斯噪声上训练的DDPM中聚合预测去噪步骤与真实反向转移的差异，在多个脑MRI数据集上显著提升了对多样化异常（尤其是MS病变）的检测性能。
摘要：
在脑MRI等医学图像中早期检测异常对于许多疾病的诊断和治疗至关重要。监督式机器学习方法受限于仅有少数病理具备良好标注数据可用性的情况。相比之下，无监督异常检测（UAD）通过发现与正常模式的偏差，有潜力识别更广泛的异常类型。我们的研究表明，现有的最先进UAD方法在现实的多模态MR数据中无法很好地泛化到多样化的异常类型。为克服这一问题，我们提出了一种新的UAD方法，名为聚合规范扩散（ANDi）。ANDi通过在去噪扩散概率模型（DDPM）中聚合预测去噪步骤与真实反向转移之间的差异来工作，这些DDPM在金字塔高斯噪声上进行了训练。我们在三个最近的UAD基线以及三个不同的脑MRI数据集上验证了ANDi。我们表明，ANDi在某些情况下大幅超越了这些基线，并表现出对不同类型的异常更强的鲁棒性。特别是在检测多发性硬化（MS）病变方面，ANDi在AUPRC指标上实现了高达178%的提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
