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

2023/07/17 by Fan Fan, Fan, Fan, Thomas DeSilvio +22
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2307.08673

openalex publication_date 2023/07/17 · openalex created_date 2023/07/19 · openalex updated_date 2026/08/01

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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