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Batch effects (BEs) refer to systematic technical differences in datacollection unrelated to biological variations whose noise is shown tonegatively impact machine learning (ML) model generalizability. Here we releaseCohortFinder, an open-source tool aimed at mitigating BEs via data-drivencohort partitioning. We demonstrate CohortFinder improves ML model performancein downstream medical image processing tasks. CohortFinder is freely availablefor download at cohortfinder.com.

CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models