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深度学习在医学影像中的应用

杨丽洋 文戈

杨丽洋, 文戈. 深度学习在医学影像中的应用[J]. 分子影像学杂志, 2020, 43(2): 183-187. doi: 10.12122/j.issn.1674-4500.2020.02.01
引用本文: 杨丽洋, 文戈. 深度学习在医学影像中的应用[J]. 分子影像学杂志, 2020, 43(2): 183-187. doi: 10.12122/j.issn.1674-4500.2020.02.01
Liyang YANG, Ge WEN. Application of deep learning in medical imaging research[J]. Journal of Molecular Imaging, 2020, 43(2): 183-187. doi: 10.12122/j.issn.1674-4500.2020.02.01
Citation: Liyang YANG, Ge WEN. Application of deep learning in medical imaging research[J]. Journal of Molecular Imaging, 2020, 43(2): 183-187. doi: 10.12122/j.issn.1674-4500.2020.02.01

深度学习在医学影像中的应用

doi: 10.12122/j.issn.1674-4500.2020.02.01
基金项目: 国家自然科学基金(81801682, 81701674);广东省自然科学基金(2020A1515010469);广东省医学科学技术研究基金(C2019077)
详细信息
    作者简介:

    杨丽洋,硕士,E-mail: 1958084765@qq.com

    通讯作者:

    文戈,主任医师,博士生导师,E-mail: 1113470826@qq.com

Application of deep learning in medical imaging research

Funds: Supported by National Natural Science Foundation of China (81801682, 81701674)
  • 摘要: 医学影像是放射科医生做出医学诊断的重要依据。但随着医学影像技术的快速发展, 逐渐增多的影像图像和复杂的图像信息对医生的工作产生了巨大的挑战。而深度学习是人工智能研究中最热门的领域, 在处理大数据和提取有效信息方面具有优势, 因此逐渐成为分析医学影像方面的首选方法。本文阐述了深度学习的概念, 并简要总结深度学习在医学影像中的常见模型, 包括卷积神经网络、循环神经网络、深度置信网络和自动编码器。卷积神经网络的基本结构是卷积层、池化层和全连接层; 循环神经网络由输入层、隐藏层和输出层组成; 深度置信网络的基础是玻尔兹曼机; 自动编码器包含编码层、隐藏层和解码层。通过对CT肺结节和MRI脑部疾病的分类, 阐明目前深度学习在疾病自动分类上准确性较高; 通过分割左心室、椎旁肌肉和肝脏的结构, 可见深度学习方法在医学图像分割上与人为分割具有一致性; 深度学习在肺结节和乳腺癌疾病的检测上已相对成熟。但目前为止, 仍存在标注的样本量少和过拟合的问题, 希望通过共享图像数据库来解决此问题。总之, 深度学习在医学影像中具有广阔前景, 且对临床医生的工作具有重大意义。

     

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  • 收稿日期:  2020-05-09
  • 刊出日期:  2020-04-15

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