Causality matters in medical imaging
File(s)
Published version
Author(s)
Coelho De Castro, D
Walker, I
Glocker, B
Type
Journal Article
Abstract
Causal reasoning can shed new light on the major challenges in ma-chine 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 Issued
2020-07-22
Date Acceptance
2020-06-29
Citation
Nature Communications, 2020, 11, pp.1-10
ISSN
2041-1723
Publisher
Nature Research (part of Springer Nature)
Start Page
1
End Page
10
Journal / Book Title
Nature Communications
Volume
11
Replaces
10044/1/81207
Copyright Statement
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Sponsor
Commission of the European Communities
Identifier
https://www.nature.com/articles/s41467-020-17478-w
Grant Number
H2020 - 757173
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
ARTIFICIAL-INTELLIGENCE
BAYESIAN NETWORKS
DIAGRAMS
INFERENCE
VALIDITY
MODEL
Causality
Diagnostic Imaging
Humans
Image Interpretation, Computer-Assisted
Machine Learning
Humans
Image Interpretation, Computer-Assisted
Diagnostic Imaging
Causality
Machine Learning
eess.IV
eess.IV
cs.AI
cs.CV
cs.LG
Publication Status
Published
Article Number
3673
Date Publish Online
2020-07-22