A Review of Causality for Learning Algorithms in Medical Image Analysis
File(s)
OA Location
Author(s)
Vlontzos, Athanasios
Rueckert, Daniel
Kainz, Bernhard
Type
Journal Article
Abstract
<jats:p>Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms.<br>We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.</jats:p>
Date Issued
2022
Date Acceptance
2022-11-01
Citation
Machine Learning for Biomedical Imaging, 1 (November 2022), pp.1-17
Publisher
Machine Learning for Biomedical Imaging
Start Page
1
End Page
17
Journal / Book Title
Machine Learning for Biomedical Imaging
Volume
1
Issue
November 2022
Copyright Statement
Copyright © 2022 The Author(s). Subject to Copyright. This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Accepted
Article Number
2022:028
Date Publish Online
2022-11-30