  Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize
anomalies without relying on annotations, only leveraging a model trained on a
dataset known to be free of anomalies. Diffusion models learn to modify inputs
$x$ to increase the probability of it belonging to a desired distribution,
i.e., they model the score function $\nabla_x \log p(x)$. Such a score function
is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a
pixel-wise anomaly score. However, diffusion models are trained to invert a
corruption process based on Gaussian noise and the learned score function is
unlikely to generalize to medical anomalies. This work addresses the problem of
how to learn a score function relevant for UAD and proposes DISYRE:
Diffusion-Inspired SYnthetic REstoration. We retain the diffusion-like pipeline
but replace the Gaussian noise corruption with a gradual, synthetic anomaly
corruption so the learned score function generalizes to medical, naturally
occurring anomalies. We evaluate DISYRE on three common Brain MRI UAD
benchmarks and substantially outperform other methods in two out of the three
tasks.
