Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Feb 10, 2023

Abstract

BACKGROUND: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features.

Authors

  • Xiang Pan
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America.
  • He Cong
    The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, PR China.
  • Xiaolei Wang
    Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering of Nankai University, Tianjin 300350, China.
  • Heng Zhang
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yuxi Ge
    Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, PR China.
  • Shudong Hu
    Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, PR China.

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