bibtext: @InProceedings{ Nav_Ensembled_MICCAI2024,
                   author = { Naval Marimont, Sergio and Siomos, Vasilis and Baugh, Matthew and Tzelepis, Christos and Kainz, Bernhard and Tarroni, Giacomo },
                   title = { { Ensembled Cold-Diffusion Restorations 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: Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by generating anomaly-free versions of test images, but are typically unable to identify subtle anomalies. Alternatively, approaches using feature modelling or self-supervised methods, such as the ones relying on synthetically generated anomalies, do not provide out-of-the-box interpretability. In this work, we present a novel method that combines the strengths of both strategies: a generative cold-diffusion pipeline (i.e., a diffusion-like pipeline which uses corruptions not based on noise) that is trained with the objective of turning synthetically-corrupted images back to their normal, original appearance. To support our pipeline we introduce a novel synthetic anomaly generation procedure, called DAG, and a novel anomaly score which ensembles restorations conditioned with different degrees of abnormality. Our method surpasses the prior state-of-the art for unsupervised anomaly detection in three different Brain MRI datasets.
title: Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
authors: Naval Marimont, Sergio Siomos, Vasilis Baugh, Matthew Tzelepis, Christos Kainz, Bernhard Tarroni, Giacomo