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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Castro DC,Walker I,Glocker B

doi

10.1038/s41467-020-17478-w

subject

Has Abstract

pub_date

2020-07-22 00:00:00

pages

3673

issue

1

issn

2041-1723

pii

10.1038/s41467-020-17478-w

journal_volume

11

pub_type

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