@misc{
 title = {CohortFinder: An open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models},
 type = {misc},
 year = {2023},
 source = {arXiv},
 id = {8d9e8882-2ee1-36ea-9eba-3888cc3c9e5f},
 created = {2023-10-25T08:56:37.677Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:37.677Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {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 CohortFinder1, 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.},
 bibtype = {misc},
 author = {Fan, F. and Martinez, G. and Desilvio, T. and Shin, J. and Chen, Y. and Wang, B. and Ozeki, T. and Lafarge, M.W. and Koelzer, V.H. and Barisoni, L. and Madabhushi, A. and Viswanath, S.E. and Janowczyk, A.}
}

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