TY  - CONF
AU  - Nieto, Nicolas
AU  - Raimondo, Federico
AU  - Patil, Kaustubh
TI  - JuHarmonize: Leakage-free data harmonization
M1  - FZJ-2023-03220
PY  - 2023
N1  - Acknowledgments: This study was supported by Helmholtz AI project DeGen and Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain.
AB  - 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.
T2  - Organization for Human Brain Mapping (OHBM)
CY  - 22 Jul 2023 - 26 Jul 2023, Montreal (Canada)
Y2  - 22 Jul 2023 - 26 Jul 2023
M2  - Montreal, Canada
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2023-03220
UR  - https://juser.fz-juelich.de/record/1014297
ER  -