Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks.

Abstract

Authors

• Qian Wang
  Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
• Xiao Han
  College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Provincial Key Laboratory of Clean Production of Fine Chemicals, Shandong Normal University Jinan 250014 China cyzhang@sdnu.edu.cn.
• Liangliang Song
  Institute of Engineering Management, Hohai University, Nanjing 211100, China.
• Xin Zhang
  First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
• Biao Zhang
  Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin 150080, China.
• Zongyun Gu
  College of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
• Bo Jiang
  Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China. 111501206@njfu.edu.cn.
• Chuanfu Li
  Medical Imaging Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, China.
• Xiaohu Li
  Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, Anhui, China.
• Yongqiang Yu
  College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.

Keywords

External Resources