Ensembled cold-diffusion restorations for unsupervised anomaly detection
OA Location
Author(s)
Type
Chapter
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.
Editor(s)
Linguraru, MG
Dou, Q
Feragen, A
Giannarou, S
Glocker, B
Lekadir, K
Schnabel, JA
Date Issued
2024-10-03
Citation
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, 2024, 15011, pp.243-253
ISBN
978-3-031-72119-9
Publisher
Springer Nature Switzerland AG
Start Page
243
End Page
253
Journal / Book Title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Lecture Notes in Computer Science
Volume
15011
Copyright Statement
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG.
Subjects
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
diffusion
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
synthetic
Technology
Unsupervised anomaly detection
Publication Status
Published
Date Publish Online
2024-10-03