bibtext: @InProceedings{ Wol_Binary_MICCAI2024,
                   author = { Wolleb, Julia and Bieder, Florentin and Friedrich, Paul and Zhang, Peter and Durrer, Alicia and Cattin, Philippe C. },
                   title = { { Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: The high performance of denoising diffusion models for image generation has also 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.
title: Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection
authors: Wolleb, Julia Bieder, Florentin Friedrich, Paul Zhang, Peter Durrer, Alicia Cattin, Philippe C.