Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

Journal: Journal of biomedical informatics
PMID: 38862083

Abstract

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement.

Authors

  • Han Yuan
    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Chuan Hong
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Peng-Tao Jiang
  • Gangming Zhao
  • Nguyen Tuan Anh Tran
    Department of Diagnostic Radiology, Singapore General Hospital, Singapore.
  • Xinxing Xu
    A*STAR, Singapore, Singapore.
  • Yet Yen Yan
    Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore.
  • Nan Liu
    Duke-NUS Medical School Centre for Quantitative Medicine Singapore Singapore.

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