Development and validation of a meta-learning-based multi-modal deep learning algorithm for detection of peritoneal metastasis.

Journal: International journal of computer assisted radiology and surgery
Published Date: Oct 1, 2022

Abstract

PURPOSE: The existing medical imaging tools have a detection accuracy of 97% for peritoneal metastasis(PM) bigger than 0.5 cm, but only 29% for that smaller than 0.5 cm, the early detection of PM is still a difficult problem. This study is aiming at constructing a deep convolution neural network classifier based on meta-learning to predict PM.

Authors

  • Hangyu Zhang
    The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Xudong Zhu
    The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Xiaomeng Dai
    Institute of Information Science and Electronic Engineering, Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang, China.
  • Xuanwen Bao
    The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Qihan Fu
    The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Zhou Tong
    Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, CN, China. zju_tz@zju.edu.cn.
  • Lulu Liu
    Analytical Center, Neurology Department of Affiliated Hospital, Institute of Neurology, Guangdong Medical University, Zhanjiang, Guangdong 524023, China. caichun2006@tom.com chenyusen925@163.com.
  • Yi Zheng
    Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.
  • Peng Zhao
    Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Luan Ye
    Institute of Information Science and Electronic Engineering, Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang, China.
  • ZhiHong Chen
    College of Information Technology and Engineering, Chengdu University, Chengdu, China.
  • Weijia Fang
    Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, CN, China.
  • Lingxiang Ruan
    The First Affiliated Hospital of Medical School of Zhejiang University, Hangzhou, China.
  • Xinyu Jin
    College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.

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