  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.
