CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models

Autor:	Fan Fan, Georgia Martinez, Thomas DeSilvio, John Shin, Yijiang Chen, Jackson Jacobs, Bangchen Wang, Takaya Ozeki, Maxime W. Lafarge, Viktor H. Koelzer, Laura Barisoni, Anant Madabhushi, Satish E. Viswanath, Andrew Janowczyk
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Medical technology R855-855.5 Medical physics. Medical radiology. Nuclear medicine R895-920
Zdroj:	npj Imaging, Vol 2, Iss 1, Pp 1-7 (2024)
Druh dokumentu:	article
ISSN:	2948-197X
DOI:	10.1038/s44303-024-00018-2
Popis:	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 ( http://cohortfinder.com ), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4fdb68ecd100482e94a7fe441fb62a63 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ