The role of noise in denoising models for anomaly detection in medical images

Kascenas, A. et al. (2023) The role of noise in denoising models for anomaly detection in medical images. Medical Image Analysis, 90, 102963. (doi: 10.1016/j.media.2023.102963) (PMID:37769551)

Abstract

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research.

• Item Type: Articles
• Additional Information: This work is part of the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) project number 104690. We acknowledge Engineering and Physical Sciences Research Council (EPSRC) for funding part of this work through the EPSRC Centre for Doctoral Training in Applied Photonics (CDTAP) managed by Heriot-Watt University. This work was supported by the University of Edinburgh, the Royal Academy of Engineering and Canon Medical Research Europe via PhD studentships of Pedro Sanchez (grant RCSRF1819\8\25). S.A. Tsaftaris acknowledges the support of Canon Medical and the Royal Academy of Engineering and the Research Chairs and Senior Research Fellowships scheme (grant RCSRF1819\8\25).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Pugeault, Dr Nicolas and Kascenas, Antanas
• Authors: Kascenas, A., Sanchez, P., Schrempf, P., Wang, C., Clackett, W., Mikhael, S. S., Voisey, J. P., Goatman, K., Weir, A., Pugeault, N., Tsaftaris, S. A., and O’Neil, A. Q.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science
• Journal Name: Medical Image Analysis
• Publisher: Elsevier
• ISSN: 1361-8415
• ISSN (Online): 1361-8423
• Published Online: 11 September 2023
• Copyright Holders: Copyright © 2023 Elsevier B.V.
• First Published: First published in Medical Image Analysis 90:102963
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304546		I-CAIRD: Industrial Centre for AI Research in Digital Diagnostics	Keith Muir	Innovate UK (INNOVATE)	104690	SPN - Centre for Stroke & Brain Imaging