Image harmonization: A review of statistical and deep learning methods for removing batch effects an...
Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
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Publisher
United States: Elsevier Inc
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Language
English
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Publisher
United States: Elsevier Inc
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Scope and Contents
Contents
•Batch effects introduce significant confounding in multi-batch neuroimaging data.•Removal of batch effects is critical for reproducibility and generalizability.•We review current harmonization methods and describe common evaluation metrics.•We provide guidance to end-users on choosing an appropriate harmonization method.•We provide guidance to met...
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Full title
Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
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TN_cdi_doaj_primary_oai_doaj_org_article_6dec96d34ba34395b20d9e3d639003fa
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_6dec96d34ba34395b20d9e3d639003fa
Other Identifiers
ISSN
1053-8119,1095-9572
E-ISSN
1095-9572
DOI
10.1016/j.neuroimage.2023.120125
