Multi-scale, domain knowledge-guided attention + random forest: a two-stage deep learning-based multi-scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images.

Journal: Medical physics
Published Date: Nov 5, 2022

Abstract

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability.

Authors

  • Wenxi Yu
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Hua Zhou
    Department of Biostatistics, UCLA.
  • Youngwon Choi
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Jonathan G Goldin
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Pangyu Teng
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Weng Kee Wong
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Michael F McNitt-Gray
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Matthew S Brown
    University of California Los Angeles David Geffen School of Medicine, Los Angeles, California, USA.
  • Grace Hyun J Kim
    Department of Biostatistics, University of California, Los Angeles, California, USA.

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