A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images With Multi-Relationship Graph Learning.

Journal: IEEE transactions on medical imaging
Published Date:

Abstract

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.

Authors

  • Mengliang Zhang
    The University of Texas Arlington, Arlington, 76010, TX, USA.
  • Xinyue Hu
    Beijing Eaglevision Technology Development, Beijing, China.
  • Lin Gu
  • Liangchen Liu
  • Kazuma Kobayashi
    Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute.
  • Tatsuya Harada
    Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan. harada@mi.t.u-tokyo.ac.jp.
  • Yan Yan
    Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.
  • Yingying Zhu
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.