CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.
Publication
, Journal Article
Fan, F; Martinez, G; DeSilvio, T; Shin, J; Chen, Y; Jacobs, J; Wang, B; Ozeki, T; Lafarge, MW; Koelzer, VH; Barisoni, L; Madabhushi, A ...
Published in: Npj Imaging
2024
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.
Duke Scholars
Published In
Npj Imaging
DOI
EISSN
2948-197X
Publication Date
2024
Volume
2
Issue
1
Start / End Page
15
Location
Switzerland
Citation
APA
Chicago
ICMJE
MLA
NLM
Fan, F., Martinez, G., DeSilvio, T., Shin, J., Chen, Y., Jacobs, J., … Janowczyk, A. (2024). CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models. Npj Imaging, 2(1), 15. https://doi.org/10.1038/s44303-024-00018-2
Fan, Fan, Georgia Martinez, Thomas DeSilvio, John Shin, Yijiang Chen, Jackson Jacobs, Bangchen Wang, et al. “CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.” Npj Imaging 2, no. 1 (2024): 15. https://doi.org/10.1038/s44303-024-00018-2.
Fan F, Martinez G, DeSilvio T, Shin J, Chen Y, Jacobs J, et al. CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models. Npj Imaging. 2024;2(1):15.
Fan, Fan, et al. “CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.” Npj Imaging, vol. 2, no. 1, 2024, p. 15. Pubmed, doi:10.1038/s44303-024-00018-2.
Fan F, Martinez G, DeSilvio T, Shin J, Chen Y, Jacobs J, Wang B, Ozeki T, Lafarge MW, Koelzer VH, Barisoni L, Madabhushi A, Viswanath SE, Janowczyk A. CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models. Npj Imaging. 2024;2(1):15.
Published In
Npj Imaging
DOI
EISSN
2948-197X
Publication Date
2024
Volume
2
Issue
1
Start / End Page
15
Location
Switzerland