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AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise

Wyatt, Julian and Leach, Adam and Schmon, Sebastian M. and Willcocks, Chris G. (2022) 'AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise.', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops New Orleans, LA, 19-24 June 2022.

Abstract

Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffusion probabilistic models (DDPMs) for unsupervised anomaly detection. DDPMs have superior mode coverage over generative adversarial networks (GANs) and higher sample quality than variational autoencoders (VAEs). However, this comes at the expense of poor scalability and increased sampling times due to the long Markov chain sequences required. We observe that within reconstruction-based anomaly detection a full-length Markov chain diffusion is not required. This leads us to develop a novel partial diffusion anomaly detection strategy that scales to high-resolution imagery, named AnoDDPM. A secondary problem is that Gaussian diffusion fails to capture larger anomalies; therefore we develop a multi-scale simplex noise diffusion process that gives control over the target anomaly size. AnoDDPM with simplex noise is shown to significantly outperform both f-AnoGAN and Gaussian diffusion for the tumorous dataset of 22 T1- weighted MRI scans (CCBS Edinburgh) qualitatively and quantitatively (improvement of +25.5% Sørensen–Dice coefficient, +17.6% IoU and +7.4% AUC).

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://ieeexplore.ieee.org/xpl/conhome/1000147/all-proceedings
Publisher statement:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Date accepted:16 May 2022
Date deposited:10 June 2022
Date of first online publication:10 June 2022
Date first made open access:24 June 2022

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