ResNet及其在医学图像处理领域的应用：研究进展与挑战

周涛; 刘赟璨; 陆惠玲; 叶鑫宇; 常晓玉

doi:10.11999/JEIT210914

ResNet及其在医学图像处理领域的应用：研究进展与挑战

doi: 10.11999/JEIT210914

周涛^{1, 2},
刘赟璨^1, ,,
陆惠玲³,
叶鑫宇¹,
常晓玉¹

1.
北方民族大学计算机科学与工程学院银川 750021
2.
北方民族大学图像图形智能处理国家民委重点实验室银川 750021
3.
宁夏医科大学理学院银川 750004

基金项目: 国家自然科学基金(62062003)，宁夏自治区重点研发计划(2020BEB04022)，北方民族大学引进人才科研启动项目(2020KYQD08)，2020年北方民族大学研究生创新项目(YCX21089)

详细信息

作者简介:
周涛：男，1977年生，教授，博士生导师，研究方向为医学图像处理、计算机辅助诊断、模式识别

刘赟璨：女，1999年生，硕士生，研究方向为医学图像处理、计算机辅助诊断

陆惠玲：女，1976年生，副教授，研究方向为医学图像分析处理、机器学习

叶鑫宇：男，1999年生，硕士生，研究方向为医学图像处理、计算机辅助诊断

常晓玉：女，1997年生，硕士生，研究方向为医学图像处理、计算机辅助诊断

通讯作者:
刘赟璨　lyc9619@163.com

中图分类号: TN911.73; TP399
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出版历程
- 收稿日期: 2021-08-31
- 修回日期: 2021-12-24
- 录用日期: 2021-12-24
- 网络出版日期: 2022-01-04
- 刊出日期: 2022-01-10

ResNet and Its Application to Medical Image Processing: Research Progress and Challenges

ZHOU Tao^{1, 2},
LIU Yuncan^{1
, ,},
LU Huiling³,
YE Xinyu¹,
CHANG Xiaoyu¹

1.
School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
2.
Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
3.
School of Science, Ningxia Medical University, Yinchuan 750004, China

Funds: The National Natural Science Foundation of China (62062003), The Key R&D Plan of Ningxia Autonomous Region (2020BEB04022), The Introduction of Talents and Scientific Research Start-Up Project of North Minzu University (2020KYQD08), The 2020 Graduate Innovation Project of North Minzu University (YCX21089)

摘要

摘要: 残差神经网络(ResNet)是深度学习领域的研究热点，广泛应用于医学图像处理领域。该文对残差神经网络从以下几个方面进行综述：首先，阐述残差神经网络的基本原理和模型结构；然后，从残差单元、残差连接和网络整体结构3方面总结了残差神经网络的改进机制；其次，从与DenseNet, U-Net, Inception结构和注意力机制结合4方面探讨残差神经网络在医学图像处理领域中的广泛应用；最后，讨论ResNet在医学图像处理领域中面临的主要挑战，并对未来的发展方向进行展望。该文系统梳理了残差神经网络的最新研究进展，以及在医学图像处理中的应用，对残差神经网络的研究具有重要的参考价值。
- 残差神经网络 /
- 医学图像 /
- 残差单元 /
- 残差连接 /
- 激活函数
Abstract: Residual neural Network (ResNet) is a hot topic in deep learning research, which is widely used in medical image processing. The residual neural network is reviewed in this paper from the following aspects: Firstly, the basic principles and model structure of residual neural network are explained; Secondly, the improvement mechanisms of residual neural network are summarized from three aspects of residual unit, residual connection and the entire network structure; Thirdly, the wide applications of residual neural network to medical image processing are discussed from four aspects combining DenseNet, U-Net, Inception structure and attention mechanism; Finally, the main challenges that ResNet faces in medical image processing are discussed, and the future development direction is prospected. In this paper, the latest research progress of residual neural network and its application to medical image processing are systematically sorted out, which has important reference value for the research of residual neural network.
- Residual neural Network (ResNet) /
- Medical image /
- Residual unit /
- Residual connection /
- Activation function

HTML全文

图 1 34层残差神经网络结构

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图 2 残差单元示意图

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图 3 残差单元框架图

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图 4 卷积层总结

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图 5 归一化算法图示

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图 6 基本激活函数

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图 7 ReLU型激活函数

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图 8 ELU型激活函数

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图 9 其它激活函数

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图 10 残差单元整体结构

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图 11 残差连接示意图

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图 12 拓扑结构

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图 13 残差神经网络整体结构

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表 1 归一化算法总结

归一化算法	归一化对象	归一化维度	计算的元素数量	与批量大小是否有关
批量归一化(BN)	激活值	一批数据的单通道	N × H × W	有关
批量重归一化(BRN)	激活值	一批数据的单通道	N × H × W	有关
层归一化(LN)	激活值	一层数据的全部通道	C × H × W	无关
实例归一化(IN)	激活值	单一通道的数据	H × W	无关
组归一化(GN)	激活值	一层数据的通道子集	\|C/G\| × H × W	无关
自适配归一化(SN)	激活值	结合通道、层和小批量	—	有关
权重归一化(WN)	权重	单个卷积核的参数	—	无关

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