Attention-based deep learning network for predicting World Health Organization meningioma grade and Ki-67 expression based on magnetic resonance imaging.

Abstract

Authors

• Xing Cheng
  Health Management Center.
• Huaning Li
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Chen Li
  School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
• Jintan Li
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Zijie Liu
  The First Clinical School of Nanjing Medical University, Nanjing, China.
• Xiao Fan
  Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China.
• Chenfei Lu
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Kefan Song
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Zhiyan Shen
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Zhichao Wang
• Qing Yang
  School of Nursing, Chengdu Medical College, Chengdu, China.
• Junxia Zhang
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
• Jianxing Yin
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Chunfa Qian
  Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China. njnkqcf@njmu.edu.cn.
• Yongping You
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
• Xiefeng Wang
  Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. xiefeng@njmu.edu.cn.

Keywords

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