A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area.

Journal: STAR protocols
Published Date: Jun 22, 2022

Abstract

We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the execution of the python project based on the dataset used in the original publication or a custom dataset. Detailed steps include data standardization, data preprocessing, model implementation, results display through heatmaps, and statistical analysis process with Origin software or python codes. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022).

Authors

  • Liuyin Chen
    School of Data Science, City University of Hong Kong, Hong Kong SAR, China. Electronic address: liuyichen4-c@my.cityu.edu.hk.
  • Haoyang Qi
    School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
  • Di Lu
    Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
  • Jianxue Zhai
    Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Kaican Cai
    Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
  • Long Wang
  • Guoyuan Liang
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Zijun Zhang
    Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, USA.

Keywords

External Resources

Popular Topics
Recent Journals