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Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

Abstract:

Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase in variance due to nonbiological factors such as the differences in acquisition protocols and hardware, which can mask signals of interest. We propose a deep learning based training scheme, inspired by domain adaptation techniques, which uses an i...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.neuroimage.2020.117689

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Sub department:
Clinical Neurosciences
Role:
Author
Publisher:
Elsevier
Journal:
NeuroImage More from this journal
Volume:
228
Issue:
March 2021
Article number:
117689
Publication date:
2020-12-29
Acceptance date:
2020-12-23
DOI:
EISSN:
1095-9572
ISSN:
1053-8119
Pmid:
33385551
Language:
English
Keywords:
Pubs id:
1152399
Local pid:
pubs:1152399
Deposit date:
2021-01-26

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