  The high performance of denoising diffusion models for image generation has
paved the way for their application in unsupervised medical anomaly detection.
As diffusion-based methods require a lot of GPU memory and have long sampling
times, we present a novel and fast unsupervised anomaly detection approach
based on latent Bernoulli diffusion models. We first apply an autoencoder to
compress the input images into a binary latent representation. Next, a
diffusion model that follows a Bernoulli noise schedule is employed to this
latent space and trained to restore binary latent representations from
perturbed ones. The binary nature of this diffusion model allows us to identify
entries in the latent space that have a high probability of flipping their
binary code during the denoising process, which indicates out-of-distribution
data. We propose a masking algorithm based on these probabilities, which
improves the anomaly detection scores. We achieve state-of-the-art performance
compared to other diffusion-based unsupervised anomaly detection algorithms
while significantly reducing sampling time and memory consumption. The code is
available at https://github.com/JuliaWolleb/Anomaly_berdiff.
