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

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arxiv 2307.08673 v1 pith:5FZ3SPFT submitted 2023-07-17 cs.LG cs.CV

Fan Fan , Georgia Martinez , Thomas Desilvio , John Shin , Yijiang Chen , Bangchen Wang , Takaya Ozeki , Maxime W. Lafarge
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Viktor H. Koelzer Laura Barisoni Anant Madabhushi Satish E. Viswanath Andrew Janowczyk
This is my paper · ORCID
classification cs.LGcs.CV
keywords cohortfinderdata-drivenimagelearningmachinemodelopen-sourcepartitioning
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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.

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