CohortFinder: an open-source tool for data-driven partitioning of
  biomedical image cohorts to yield robust machine learning models
Fan Fan Georgia Martinez Thomas Desilvio John Shin Yijiang Chen Bangchen Wang Takaya Ozeki Maxime W. Lafarge Viktor H. Koelzer Laura Barisoni Anant Madabhushi Satish E. Viswanath Andrew Janowczyk
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.