电子学报 ›› 2020, Vol. 48 ›› Issue (7): 1436-1447.DOI: 10.3969/j.issn.0372-2112.2020.07.024

所属专题: 机器学习与智慧医疗

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残差神经网络及其在医学图像处理中的应用研究

周涛1,2,3, 霍兵强1, 陆惠玲2, 任海玲4   

  1. 1. 北方民族大学计算机科学与工程学院, 宁夏银川 750021;
    2. 宁夏医科大学理学院, 宁夏银川 750004;
    3. 宁夏智能信息与大数据处理重点实验室, 宁夏银川 750021;
    4. 宁夏医科大学公共卫生与管理学院, 宁夏银川 750004
  • 收稿日期:2019-07-19 修回日期:2020-01-11 出版日期:2020-07-25 发布日期:2020-07-25
  • 通讯作者: 周涛
  • 作者简介:霍兵强 男,1994年生,河北石家庄人.硕士,现为北方民族大学计算机学院研究生,主研领域:智能医学影像图像处理,深度学习.E-mail:2916656832@qq.com;陆惠玲 女,1976年,河北定兴县人.副教授,硕士,主要研究医学图像分析处理、机器学习;任海玲 女,1993生,四川人.现为宁夏医科大学公共卫生与管理学院研究生,主研领域为智能医学影像图像处理及计算机辅助诊断.E-mail:renhailingnxmu@163.com
  • 基金资助:
    国家自然科学基金(No.61561040);宁夏高等学校一流学科建设(数学学科)(No.NXYLXK2017B09);北方民族大学引进人才科研启动项目(No.2020KYQD08);宁夏312优秀人才项目;北方民族大学创新创业项目(No.YCX19075)

Research on Residual Neural Network and Its Application on Medical Image Processing

ZHOU Tao1,2,3, HUO Bing-qiang1, LU Hui-ling2, REN Hai-ling4   

  1. 1. School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia 750021, China;
    2. School of Science, Ningxia Medical University, Yinchuan, Ningxia 750004, China;
    3. Ningxia Key Laboratory of Intelligent Information and Big Data Processing, Yinchuan, Ningxia 750021, China;
    4. School of Public Health and Management, Ningxia Medical University, Yinchuan, Ningxia 750004, China
  • Received:2019-07-19 Revised:2020-01-11 Online:2020-07-25 Published:2020-07-25

摘要: 残差神经网络(ResNet)是近几年来深度学习研究中的热点,在计算机视觉领域取得较好成就.本文对残差神经网络从以下几个方面进行总结:第一,阐述残差神经网络的基本结构和工作原理;第二,在模型发展方面,以时间为顺序总结了残差神经网络的8种网络模型;第三,在结构优化方面,从残差神经网络的卷积层、池化层、残差单元、全连接层以及整个网络5个方面进行总结;最后,将ResNet应用到医学图像处理领域,主要从图像识别和图像分割2个方面探讨.本文对残差神经网络的原理、模型、结构进行了系统地总结,对残差神经网络的研究发展具有一定的积极意义.

关键词: 残差神经网络, 网络结构, 医学图像

Abstract: Residual neural network (ResNet) has witnessed tremendous amount of attention in deep learning research over the last few years and has made great achievements in computer vision.In this paper,the ResNet is summarized in the following aspects:Firstly,the basic structure and working principle of the ResNet are expounded;Secondly,in model development,the eight network models of the ResNet are summarized in time sequence;Thirdly,in structural optimization,the research progress is described from five aspects of ResNet,including convolutional layer,pooling layer,residual unit,fully connected layer and the whole network;Finally,the application of ResNet in medical images processing is mainly discussed from two aspects of image recognition and image segmentation.In this paper,the principles,models,and structures of ResNet are systematically summarized,which has positive significance to the research and development of ResNet.

Key words: residual neural network, network structure, medical image

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