Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset
Highlights • Multi-site MRI data encode confounding information which may mask case-control differences.• The impact of the NeuroHarmonize method is evaluated in the ASD-control classification.• We verified the successful removal of the site effect by the harmonization protocol.• The increment in the classification performance is quantified after data harmonization.• We identified the anatomical features that contributed to the two-class separation.
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Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset
Semantic Scholar · Medicine · 2022
Abstract
Highlights
- Multi-site MRI data encode confounding information which may mask case-control differences.
- The impact of the NeuroHarmonize method is evaluated in the ASD-control classification.
- We verified the successful removal of the site effect by the harmonization protocol.
- The increment in the classification performance is quantified after data harmonization.
- We identified the anatomical features that contributed to the two-class separation.