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@INPROCEEDINGS{Nieto:1014297,
author = {Nieto, Nicolas and Raimondo, Federico and Patil, Kaustubh},
title = {{J}u{H}armonize: {L}eakage-free data harmonization},
reportid = {FZJ-2023-03220},
year = {2023},
note = {Acknowledgments: This study was supported by Helmholtz AI
project DeGen and Helmholtz Portfolio Theme Supercomputing
and Modeling for the Human Brain.},
abstract = {Combining datasets is desirable when building machine
learning models. Differences in data acquisition present
undesired variability undermining subsequent machine
learning performance. Data harmonization methods such as
ComBat can be employed, however, the requirement of test set
labels causes data leakage and prevents real-world
deployment. We propose a method called JuHarmonize that
harmonizes data without those issues.},
month = {Jul},
date = {2023-07-22},
organization = {Organization for Human Brain Mapping
(OHBM), Montreal (Canada), 22 Jul 2023
- 26 Jul 2023},
subtyp = {After Call},
cin = {INM-7},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
JL SMHB - Joint Lab Supercomputing and Modeling for the
Human Brain (JL SMHB-2021-2027)},
pid = {G:(DE-HGF)POF4-5254 / G:(DE-Juel1)JL SMHB-2021-2027},
typ = {PUB:(DE-HGF)24},
doi = {10.34734/FZJ-2023-03220},
url = {https://juser.fz-juelich.de/record/1014297},
}