  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.
