Recognizing pathology of renal tumor from macroscopic cross-section image by deep learning.

Journal: Biomedical engineering online
Published Date:

Abstract

OBJECTIVES: This study aims to develop and evaluate the deep learning-based classification model for recognizing the pathology of renal tumor from macroscopic cross-section image.

Authors

  • Zefang Lin
    Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, Guangdong 519000, PR China. Electronic address: linzef@m.scnu.edu.cn.
  • Weihong Yang
    Department of Medical Equipment Engineering, Zhuhai People's Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China.
  • Wenqiang Zhang
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China. Electronic address: zhang_wenqiang@foxmail.com.
  • Chao Jiang
    Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China.
  • Jing Chu
    Department of Gynaecology and Obstetrics, Changzheng Hospital, Naval Medical University, Shanghai, China.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Xiaoxu Yuan
    Department of Urology, Zhuhai People's Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China. chnk200210ydp@163.com.