Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.

Journal: Medical physics
Published Date: Feb 26, 2020

Abstract

PURPOSE: In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two-stage deep learning strategy for this task: prior-feature learning followed by adaptive-boost deep learning.

Authors

  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.
  • Xiaorong Chen
    Medical Imaging Department, Jinhua Municipal Central Hospital, Jinhua, 321001, China.
  • Hongbing Lu
    The Fourth Medical University, Department of of Biomedical Engineering, Xi'an, China.
  • Lichi Zhang
    School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Jianfeng Pan
    Medical Imaging Department, Jinhua Municipal Central Hospital, Jinhua, 321001, China.
  • Yong Bao
    Changzhou Industrial Technology Research Institute of Zhejiang University, Changzhou, 213022, China.
  • Jiner Su
    Medical Imaging Department, Jinhua Municipal Central Hospital, Jinhua, 321001, China.
  • Dahong Qian

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