Imputation-Based Harmonization Mitigates Site Effects Without Data Leakage in Machine Learning Studies
Noah Hillman, Andrew Chen, Fengling Hu, Simon Vandekar, Randa Melhem, Lori L. Beason‐Held, Theodore Satterthwaite, Christos A. Davatzikos, Haochang Shou, Russell T. Shinohara, Alzheimer’s Disease Neuroimaging Initiative
The paper introduces MIRTH, an imputation-based harmonization method that mitigates site effects in neuroimaging data without data leakage, achieving high predictive accuracy in machine learning studies.
bioRxiv · Oct 5, 2026 · PreprintNeuroscience