A vision transformer-convolutional neural network framework for decision-transparent dual-energy X-ray absorptiometry recommendations using chest low-dose CT.

Journal: International journal of medical informatics
PMID: 40187299

Abstract

OBJECTIVE: This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations for dual-energy X-ray absorptiometry (DXA) scans from chest low-dose computed tomography (LDCT).

Authors

  • Duen-Pang Kuo
    Department of Medical Imaging, Taipei Medical University Hospital, No.250, Wu-Hsing St, Taipei, 11031, Taiwan.
  • Yung-Chieh Chen
    Department of Medical Imaging, Taipei Medical University Hospital, No.250, Wu-Hsing St, Taipei, 11031, Taiwan.
  • Sho-Jen Cheng
    Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan.
  • Kevin Li-Chun Hsieh
    Department of Medical Imaging, Taipei Medical University Hospital, No.250, Wu Hsing Street, Taipei, Taiwan.
  • Yi-Tien Li
    Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
  • Po-Chih Kuo
    Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
  • Yung-Chun Chang
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
  • Cheng-Yu Chen
    Department of Medical Imaging, Taipei Medical University Hospital, No.250, Wu-Hsing St, Taipei, 11031, Taiwan. sandy0932@gmail.com.

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