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 non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pi

SUBMITTER: Marzi C 

PROVIDER: S-EPMC10805868 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature