Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets
Abstract Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with n...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02421-7 |