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 Communjournal_title
Nature communicationsauthors
Castro DC,Walker I,Glocker Bdoi
10.1038/s41467-020-17478-wsubject
Has Abstractpub_date
2020-07-22 00:00:00pages
3673issue
1issn
2041-1723pii
10.1038/s41467-020-17478-wjournal_volume
11pub_type
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