Anatomical Partition-Based Deep Learning: An Automatic Nasopharyngeal MRI Recognition Scheme.

Journal: Journal of magnetic resonance imaging : JMRI
PMID: 35157782

Abstract

BACKGROUND: Training deep learning (DL) models to automatically recognize diseases in nasopharyngeal MRI is a challenging task, and optimizing the performance of DL models is difficult.

Authors

  • Song Li
    Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
  • Hong-Li Hua
    Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
  • Fen Li
    College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China.
  • Yong-Gang Kong
    Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
  • Zhi-Ling Zhu
    Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Sheng-Lan Li
    Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Xi-Xiang Chen
    Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Yu-Qin Deng
    Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
  • Ze-Zhang Tao
    Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China.

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