Causality matters in medical imaging

Daniel C. Castro (), Ian Walker and Ben Glocker ()
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Daniel C. Castro: Imperial College London
Ian Walker: Imperial College London
Ben Glocker: Imperial College London

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-17478-w

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