Causality matters in medical imaging

Abstract

Causal reasoning can shed new light on the major challenges in machine learning for medical imaging: scarcity of high-quality annotated data and mismatch between the development dataset and the target environment. A causal perspective on these issues allows decisions about data collection, annotation, preprocessing, and learning strategies to be made and scrutinized more transparently, while providing a detailed categorisation of potential biases and mitigation techniques. Along with worked clinical examples, we highlight the importance of establishing the causal relationship between images and their annotations, and offer step-by-step recommendations for future studies.

Scarcity of high-quality annotated data and mismatch between the development dataset and the target environment are two of the main challenges in developing predictive tools from medical imaging. In this Perspective, the authors show how causal reasoning can shed new light on these challenges.