Back to Search Start Over

DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection

Authors :
Marimont, Sergio Naval
Baugh, Matthew
Siomos, Vasilis
Tzelepis, Christos
Kainz, Bernhard
Tarroni, Giacomo
Publication Year :
2023

Abstract

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.<br />Comment: 5 pages, 3 figures. Accepted for publication in ISBI 2024

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2311.15453
Document Type :
Working Paper