Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study.

Journal: JMIR formative research
PMID: 39636692

Abstract

BACKGROUND: While deep learning classifiers have shown remarkable results in detecting chest X-ray (CXR) pathologies, their adoption in clinical settings is often hampered by the lack of transparency. To bridge this gap, this study introduces the neural prototype tree (NPT), an interpretable image classifier that combines the diagnostic capability of deep learning models and the interpretability of the decision tree for CXR pathology detection.

Authors

  • Hongbo Chen
    Indiana University Bloomington.
  • Myrtede Alfred
    Department of Mechanical & Industrial Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada.
  • Andrew D Brown
    Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. Electronic address: andrew.brown@sloan.mit.edu.
  • Angela Atinga
    Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
  • Eldan Cohen
    Department of Mechanical & Industrial Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada.

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