Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by generating anomaly-free versions of test images, but are typically unable to identify subtle anomalies. Alternatively, approaches using feature modelling or self-supervised methods, such as the ones relying on synthetically generated anomalies, do not provide out-of-the-box interpretability. In this work, we present a novel method that combines the strengths of both strategies: a generative cold-diffusion pipeline (i.e., a diffusion-like pipeline which uses corruptions not based on noise) that is trained with the objective of turning synthetically-corrupted images back to their normal, original appearance. To support our pipeline we introduce a novel synthetic anomaly generation procedure, called DAG, and a novel anomaly score which ensembles restorations conditioned with different degrees of abnormality. Our method surpasses the prior state-of-the art for unsupervised anomaly detection in three different Brain MRI datasets.

翻译：无监督异常检测方法旨在通过将测试样本与从已知无异常数据集中学到的规范分布进行比较，以识别其中的异常。基于生成模型的方法通过生成测试图像的无异常版本提供可解释性，但通常难以识别细微异常。相反，采用特征建模或自监督方法（例如依赖合成生成异常的方法）则无法提供开箱即用的可解释性。本研究提出一种融合两种策略优势的新方法：构建基于生成式冷扩散的流程（即使用非噪声型损坏的类扩散流程），该流程以将合成损坏图像恢复至正常原始外观为目标进行训练。为支撑该流程，我们引入了一种称为DAG的新型合成异常生成程序，以及一种集成不同异常程度条件修复结果的新型异常评分方法。我们的方法在三个不同的脑部MRI数据集上超越了当前无监督异常检测的最先进水平。