Causality matters in medical imaging.

Nature Communications
Daniel C CastroBen Glocker

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.

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Citations

Feb 4, 2021·Molecular Oncology·Davide CirilloAlfonso Valencia
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Aug 30, 2021·Medical Physics·Kathryn E KeenanNandita M deSouza
Mar 23, 2021··Quaid MorrisLaleh Seyyed-Kalantari

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