A Review of Causality for Learning Algorithms in Medical Image Analysis

TL;DR

This paper reviews how causal analysis methods can improve the robustness and clinical applicability of machine learning algorithms in medical image analysis, highlighting current limitations and potential benefits.

Contribution

It provides a comprehensive review of causal analysis techniques in medical imaging AI and discusses their potential to address biases and improve clinical translation.

Findings

Causal analysis can mitigate domain shift issues in medical imaging.

Uptake of causal methods in clinical research remains limited.

Causal approaches have potential to enhance robustness and adaptability.

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.