Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.

Journal: BMC medical imaging
PMID: 38641591

Abstract

BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States.

Authors

  • Joseph Chang
    University of Passau, Passau, Germany.
  • Bo-Ru Lin
    The Data Science Degree Program, College of Electrical Engineering and Computer Science, National Taiwan University and Academia Sinica, Taipei, Taiwan.
  • Ti-Hao Wang
    Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan. thothwang@gmail.com.
  • Chung-Ming Chen
    Institute of Biomedical Engineering, National Taiwan University, Taipei 100, Taiwan.

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