  Unsupervised Anomaly Detection (UAD) methods rely on healthy data
distributions to identify anomalies as outliers. In brain MRI, a common
approach is reconstruction-based UAD, where generative models reconstruct
healthy brain MRIs, and anomalies are detected as deviations between input and
reconstruction. However, this method is sensitive to imperfect reconstructions,
leading to false positives that impede the segmentation. To address this
limitation, we construct multiple reconstructions with probabilistic diffusion
models. We then analyze the resulting distribution of these reconstructions
using the Mahalanobis distance to identify anomalies as outliers. By leveraging
information about normal variations and covariance of individual pixels within
this distribution, we effectively refine anomaly scoring, leading to improved
segmentation. Our experimental results demonstrate substantial performance
improvements across various data sets. Specifically, compared to relying solely
on single reconstructions, our approach achieves relative improvements of
15.9%, 35.4%, 48.0%, and 4.7% in terms of AUPRC for the BRATS21, ATLAS, MSLUB
and WMH data sets, respectively.
