Unsupervised 3D out-of-distribution detection with latent diffusion
  models
Mark S. Graham Walter Hugo Lopez Pinaya Paul Wright Petru-Daniel Tudosiu Yee H. Mah James T. Teo H. Rolf Jäger David Werring Parashkev Nachev Sebastien Ourselin M. Jorge Cardoso
Methods for out-of-distribution (OOD) detection that scale to 3D data are
crucial components of any real-world clinical deep learning system. Classic
denoising diffusion probabilistic models (DDPMs) have been recently proposed as
a robust way to perform reconstruction-based OOD detection on 2D datasets, but
do not trivially scale to 3D data. In this work, we propose to use Latent
Diffusion Models (LDMs), which enable the scaling of DDPMs to high-resolution
3D medical data. We validate the proposed approach on near- and far-OOD
datasets and compare it to a recently proposed, 3D-enabled approach using
Latent Transformer Models (LTMs). Not only does the proposed LDM-based approach
achieve statistically significant better performance, it also shows less
sensitivity to the underlying latent representation, more favourable memory
scaling, and produces better spatial anomaly maps. Code is available at
https://github.com/marksgraham/ddpm-ood