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| 001 | 1014297 | ||
| 005 | 20230829205459.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2023-03220 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-03220 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Nieto, Nicolas |0 P:(DE-Juel1)194707 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Organization for Human Brain Mapping (OHBM) |c Montreal |d 2023-07-22 - 2023-07-26 |w Canada |
| 245 | _ | _ | |a JuHarmonize: Leakage-free data harmonization |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1693297726_22959 |2 PUB:(DE-HGF) |x After Call |
| 500 | _ | _ | |a Acknowledgments: This study was supported by Helmholtz AI project DeGen and Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain. |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
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| 700 | 1 | _ | |a Raimondo, Federico |0 P:(DE-Juel1)185083 |b 1 |
| 700 | 1 | _ | |a Patil, Kaustubh |0 P:(DE-Juel1)172843 |b 2 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1014297/files/Poster_Nieto_OHBM_2023.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2023 |
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