  Batch effects (BEs) refer to systematic technical differences in data
collection unrelated to biological variations whose noise is shown to
negatively impact machine learning (ML) model generalizability. Here we release
CohortFinder, an open-source tool aimed at mitigating BEs via data-driven
cohort partitioning. We demonstrate CohortFinder improves ML model performance
in downstream medical image processing tasks. CohortFinder is freely available
for download at cohortfinder.com.
