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深度学习在骨科领域的应用研究进展

刘恩情 谭雪梅 蒋子月 黄成颉 张伟聪 吕海 苏志海

刘恩情, 谭雪梅, 蒋子月, 黄成颉, 张伟聪, 吕海, 苏志海. 深度学习在骨科领域的应用研究进展[J]. 分子影像学杂志, 2022, 45(1): 151-156. doi: 10.12122/j.issn.1674-4500.2022.01.30
引用本文: 刘恩情, 谭雪梅, 蒋子月, 黄成颉, 张伟聪, 吕海, 苏志海. 深度学习在骨科领域的应用研究进展[J]. 分子影像学杂志, 2022, 45(1): 151-156. doi: 10.12122/j.issn.1674-4500.2022.01.30
LIU Enqing, TAN Xuemei, JIANG Ziyue, HUANG Chengjie, ZHANG Weicong, LÜ Hai, SU Zhihai. Advances in the application of deep learning in orthopedics[J]. Journal of Molecular Imaging, 2022, 45(1): 151-156. doi: 10.12122/j.issn.1674-4500.2022.01.30
Citation: LIU Enqing, TAN Xuemei, JIANG Ziyue, HUANG Chengjie, ZHANG Weicong, LÜ Hai, SU Zhihai. Advances in the application of deep learning in orthopedics[J]. Journal of Molecular Imaging, 2022, 45(1): 151-156. doi: 10.12122/j.issn.1674-4500.2022.01.30

深度学习在骨科领域的应用研究进展

doi: 10.12122/j.issn.1674-4500.2022.01.30
基金项目: 

珠海市创新创业团队项目 ZH0406190031PWC

详细信息
    作者简介:

    刘恩情,E-mail: 854015997@qq.com

    通讯作者:

    吕海,主任医师,博士生导师,E-mail: lvhai@mail.sysu.edu.cn

    苏志海,博士,住院医师,E-mail: suzhh6@mail2.sysu.edu.cn

Advances in the application of deep learning in orthopedics

  • 摘要: 深度学习是当前人工智能发展最为迅速的一个分支。深度学习可以在大样本数据中自动提取良好的特征表达,有效提升各种机器学习的任务性能,广泛应用于图像信号处理、计算机视觉和自然语言处理等领域。随着数字影像的发展,深度学习凭借自动提取特征,高效处理高维度医学图像数据的优点,已成为医学图像分析在临床应用的重要技术之一。目前这项技术在分析某些医学影像方面已达到放射科医生水平,如肺结节的检出识别以及对膝关节退变进行级别分类等,这将为计算机科学发展在医疗应用的提供一个新机遇。由于骨科领域疾病种类繁多,图像数据特征清晰,内容复杂丰富,相关的学习任务与应用场景对深度学习提出了新要求。本文将从骨关节关键参数测量、病灶检测、疾病分级、图像分割以及图像配准五大临床图像处理分析任务对深度学习在骨科领域的应用研究进展进行综述,并对其发展趋势进行展望,以供从事骨科相关研究人员作参考。

     

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出版历程
  • 收稿日期:  2021-12-21
  • 网络出版日期:  2022-03-29
  • 刊出日期:  2022-01-20

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