A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification.

Journal: International journal of computer assisted radiology and surgery
Published Date: Nov 25, 2019

Abstract

PURPOSE: Diagnosis of lung cancer requires radiologists to review every lung nodule in CT images. Such a process can be very time-consuming, and the accuracy is affected by many factors, such as experience of radiologists and available diagnosis time. To address this problem, we proposed to develop a deep learning-based system to automatically classify benign and malignant lung nodules.

Authors

  • Ying Ren
    Department of Radiology, Shengjing Hospital of China Medical University Shenyang 110004 P. R. China.
  • Min-Yu Tsai
    Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
  • Liyuan Chen
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Shulong Li
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.
  • Yufei Liu
    China Agricultural University, Beijing 100083, China.
  • Xun Jia
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75235.
  • Chenyang Shen
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75002, USA.

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