Your browser doesn't support javascript.
loading
CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.
Fan, Fan; Martinez, Georgia; DeSilvio, Thomas; Shin, John; Chen, Yijiang; Jacobs, Jackson; Wang, Bangchen; Ozeki, Takaya; Lafarge, Maxime W; Koelzer, Viktor H; Barisoni, Laura; Madabhushi, Anant; Viswanath, Satish E; Janowczyk, Andrew.
Afiliação
  • Fan F; Emory University and Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, GA USA.
  • Martinez G; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA.
  • DeSilvio T; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA.
  • Shin J; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA.
  • Chen Y; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA.
  • Jacobs J; Emory University and Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, GA USA.
  • Wang B; Duke University, Department of Pathology, Division of AI & Computational Pathology, Durham, NC USA.
  • Ozeki T; University of Michigan, Department of Internal Medicine, Division of Nephrology, Ann Arbor, MI USA.
  • Lafarge MW; University Hospital of Zurich, University of Zurich, Department of Pathology and Molecular Pathology, Zurich, Switzerland.
  • Koelzer VH; University Hospital of Zurich, University of Zurich, Department of Pathology and Molecular Pathology, Zurich, Switzerland.
  • Barisoni L; Duke University, Department of Pathology, Division of AI & Computational Pathology, Durham, NC USA.
  • Madabhushi A; Duke University, Department of Medicine, Division of Nephrology, Durham, NC USA.
  • Viswanath SE; Emory University and Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, GA USA.
  • Janowczyk A; Atlanta Veterans Administration Medical Center, Atlanta, GA USA.
Npj Imaging ; 2(1): 15, 2024.
Article em En | MEDLINE | ID: mdl-38962496
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.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article