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

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

BackgroundWhile 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...

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Bibliographic Details
Main Authors: Hongbo Chen, Myrtede Alfred, Andrew D Brown, Angela Atinga, Eldan Cohen
Format: Article
Language:English
Published: JMIR Publications 2024-12-01
Series:JMIR Formative Research
Online Access:https://formative.jmir.org/2024/1/e59045
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