Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

Journal: BMJ open
Published Date: Jul 25, 2023

Abstract

OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes.

Authors

  • YanLi Zhao
    Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
  • Sen He
    Digital Manufacturing Laboratory, Beijing Institute of Technology, Beijing, China.
  • Dan Zhao
    Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, China.
  • Mengwei Ju
    School of Information Science and Technology, Beijing Forestry University, Beijing, China.
  • Caiwei Zhen
    School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
  • Yujie Dong
    Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, China.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Lang Wang
    Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, China.
  • Shuhao Wang
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, P. R. China.
  • Nanying Che
    Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, China cheny0448@163.com.

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