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基于深度学习和组织病理图像的癌症分类研究进展

颜锐 陈丽萌 李锦涛 任菲

颜锐, 陈丽萌, 李锦涛, 任菲. 基于深度学习和组织病理图像的癌症分类研究进展[J]. 协和医学杂志, 2021, 12(5): 742-748. doi: 10.12290/xhyxzz.2021-0452
引用本文: 颜锐, 陈丽萌, 李锦涛, 任菲. 基于深度学习和组织病理图像的癌症分类研究进展[J]. 协和医学杂志, 2021, 12(5): 742-748. doi: 10.12290/xhyxzz.2021-0452
YAN Rui, CHEN Limeng, LI Jintao, REN Fei. Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 742-748. doi: 10.12290/xhyxzz.2021-0452
Citation: YAN Rui, CHEN Limeng, LI Jintao, REN Fei. Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 742-748. doi: 10.12290/xhyxzz.2021-0452

基于深度学习和组织病理图像的癌症分类研究进展

doi: 10.12290/xhyxzz.2021-0452
基金项目: 

国家自然科学基金 82072939

详细信息
    通讯作者:

    任菲  电话:010-62600343,E-mail: renfei@ict.ac.cn

  • 中图分类号: R73;R445;TP391

Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

Funds: 

National Natural Science Foundation of China 82072939

More Information
    Corresponding author: REN Fei   Tel: 86-10-62600343, E-mail: renfei@ict.ac.cn
  • 摘要: 癌症的精确分类直接关系到患者治疗方案的选择和预后。病理诊断是癌症诊断的金标准,病理图像的数字化和深度学习的突破性进展使得计算机辅助癌症诊断和预后预测成为可能。本文通过简述病理图像分类常用的4种深度学习方法,总结基于深度学习和组织病理图像的癌症分类最新研究进展,指出该领域研究中普遍存在的问题与挑战,并对未来可能的发展方向进行展望。
    作者贡献:颜锐、陈丽萌、李锦涛、任菲负责论文选题构思和校对;颜锐、任菲负责文献收集,论文撰写、修订。
    利益冲突:
  • 图  1  不同尺度病理图像示意图

    图  2  基于深度学习的病理图像(Patch)分类方法的典型框架

    图  3  基于深度学习的病理图像(Image)分类方法的典型框架[28]

    图  4  基于深度学习的病理图像(WSI)分类方法的典型框架[30]

    表  1  4种常用的深度学习方法的临床应用

    病理图像 深度学习方法 方法类型 临床应用领域
    Patch CNN 监督学习 乳腺癌[20-22]
    Image CNN 监督学习 乳腺癌[23-26],结/直肠癌[27]
    CNN+RNN 监督学习 乳腺癌[28]
    CNN+GCN 监督学习 结/直肠癌[29]
    WSI CNN 监督学习 肺癌[30], 前列腺癌[31], 结肠癌[32]
    CNN+MIL 弱监督学习 胃癌[33], 结肠癌[34]
    CNN+GCN+MIL 弱监督学习 结/直肠癌[35]
    CNN+MIL+RNN 弱监督学习 前列腺癌, 皮肤癌和腋窝淋巴结[36]
    CNN:卷积神经网络;RNN:循环神经网络;GCN:图卷积神经网络;MIL:多示例学习
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-07
  • 录用日期:  2021-07-29
  • 网络出版日期:  2021-09-16
  • 刊出日期:  2021-10-12

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