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arXiv · 2307.08673

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
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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 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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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. 2023-07-17. CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models. https://arxiv.org/abs/2307.08673

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