A Review of Causality for Learning Algorithms in Medical Image Analysis

Athanasios Vlontzos10000-0002-7672-2574, Daniel Rueckert2,10000-0002-5683-5889, Bernhard Kainz3,10000-0002-7813-5023
1: Imperial College London, 2: TU Munchen, 3: FAU Erlangen-Nurnberg
November 2022 issue
Publication date: 2022/11/30
PDF · arXiv

Abstract

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.
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.

Keywords

Causality · Medical Imaging · Machine Learning

Bibtex @article{melba:2022:028:vlontzos, title = "A Review of Causality for Learning Algorithms in Medical Image Analysis", author = "Vlontzos, Athanasios and Rueckert, Daniel and Kainz, Bernhard", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "November 2022 issue", year = "2022", pages = "1--17", issn = "2766-905X", url = "https://melba-journal.org/2022:028" }
RISTY - JOUR AU - Vlontzos, Athanasios AU - Rueckert, Daniel AU - Kainz, Bernhard PY - 2022 TI - A Review of Causality for Learning Algorithms in Medical Image Analysis T2 - Machine Learning for Biomedical Imaging VL - 1 IS - November 2022 issue SP - 1 EP - 17 SN - 2766-905X UR - https://melba-journal.org/2022:028 ER -

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