[1]王霄,朱恩照,艾自胜.卷积神经网络的原理及其在医学影像诊断中的应用[J].中国医学物理学杂志,2022,39(12):1485-1489.[doi:DOI:10.3969/j.issn.1005-202X.2022.12.005]
　WANG Xiao,ZHU Enzhao,AI Zisheng.Principle of convolutional neural network and its applications in medical imaging diagnosis[J].Chinese Journal of Medical Physics,2022,39(12):1485-1489.[doi:DOI:10.3969/j.issn.1005-202X.2022.12.005]
点击复制

卷积神经网络的原理及其在医学影像诊断中的应用()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第12期
页码:
1485-1489
栏目:
医学影像物理
出版日期:
2022-12-25

文章信息/Info

Title:
Principle of convolutional neural network and its applications in medical imaging diagnosis
文章编号:
1005-202X（2022）12-1485-05
作者:
王霄; 朱恩照; 艾自胜
同济大学医学院医学统计学教研室， 上海 200092
Author(s):
WANG Xiao;  ZHU Enzhao;  AI Zisheng
Department of Medical Statistics, School of Medicine, Tongji University, Shanghai 200092, China
关键词:
人工智能; 卷积神经网络; 医学影像; 医学诊断; 综述
Keywords:
Keywords: artificial intelligence convolutional neural network medical imaging medical diagnosis review
分类号:
R318；TP183
DOI:
DOI:10.3969/j.issn.1005-202X.2022.12.005
文献标志码:
A
摘要:
利用卷积神经网络快速高效地对医学影像数据进行分析和处理可以实现医学影像数据快速分类、定位等操作，提高医学诊疗的效率。本研究从卷积神经网络的背景和原理入手，介绍各种类型的卷积神经网络的应用场景和一些常用的卷积神经网络模型，包括残差卷积神经网络、U-net、循环卷积神经网络等及其在医学影像诊断中的应用，最后针对卷积神经网络和人工智能技术讨论了其未来的展望和挑战。
Abstract:
Abstract: Convolutional neural network can realize the localization, classification and other tasks for the large amounts of medical imaging data by analyzing and processing the medical imaging data swiftly and efficiently, thereby improving the efficiency of medical diagnosis and treatment. Starting from the introduction on the background and principle of convolutional neural network, the application scenarios of various types of convolutional neural networks are presented, and the applications of some commonly-used neural network models, including residual convolutional neural network, U-net, recurrent convolutional neural network in medical imaging diagnosis are summarized. Finally, the future prospects and challenges of convolutional neural network and artificial intelligence are discussed.

相似文献/References:

[1]王弈,李传富.人工智能方法在医学图像处理中的研究新进展[J].中国医学物理学杂志,2013,30(03):4138.[doi:10.3969/j.issn.1005-202X.2013.03.013]
[2]王亚,李永欣,黄文华.人类脑计划的研究进展[J].中国医学物理学杂志,2016,33(2):109.[doi:10.3969/j.issn.1005-202X.2016.02.001]
　[J].Chinese Journal of Medical Physics,2016,33(12):109.[doi:10.3969/j.issn.1005-202X.2016.02.001]
[3]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
　[J].Chinese Journal of Medical Physics,2017,34(12):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[4]祁红琳,胡先玲,李传明,等. 基于MRI纹理特征的早期肝癌术后复发预测[J].中国医学物理学杂志,2017,34(9):908.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.010]
　[J].Chinese Journal of Medical Physics,2017,34(12):908.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.010]
[5]刘岩,李幼军,陈萌. 基于固有模态分解和深度学习的抑郁症脑电信号分类分析[J].中国医学物理学杂志,2017,34(9):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
　[J].Chinese Journal of Medical Physics,2017,34(12):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
[6]张俊,朱金汉,庄永东,等. 基于卷积神经网络CT/CBCT影像质量自动分析[J].中国医学物理学杂志,2018,35(5):557.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.012]
　ZHANG Jun,ZHU Jinhan,ZHUANG Yongdong,et al. Automatic analysis of CT/CBCT image quality based on convolutional neural network[J].Chinese Journal of Medical Physics,2018,35(12):557.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.012]
[7]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
　DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(12):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[8]申代友,库洪安,皮红英,等. 基于深度相机的老年跌倒监护系统[J].中国医学物理学杂志,2019,36(2):223.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.019]
　SHEN Daiyou,KU Hongan,PI Hongying,et al. Depth camera-based fall detection system for the elderly[J].Chinese Journal of Medical Physics,2019,36(12):223.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.019]
[9]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(12):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[10]王自强,刘洪运,石金龙,等.基于卷积神经网络的心电图心博识别[J].中国医学物理学杂志,2019,36(8):938.[doi:DOI:10.3969/j.issn.1005-202X.2019.08.015]
　WANG Ziqiang,LIU Hongyun,SHI Jinlong,et al.ECG heartbeat recognition based on convolution neural network[J].Chinese Journal of Medical Physics,2019,36(12):938.[doi:DOI:10.3969/j.issn.1005-202X.2019.08.015]
[11]纪春阳,徐秀林,王燕. 深度神经网络技术在肿瘤细胞识别中的应用[J].中国医学物理学杂志,2019,36(9):1113.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.022]
　JI Chunyang,XU Xiulin,WANG Yan. Application of deep neural network in tumor cell recognition[J].Chinese Journal of Medical Physics,2019,36(12):1113.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.022]
[12]曹洋森,朱晓斐,韩妙飞,等.基于级联式深度网络模型的胃及胰腺自动分割研究[J].中国医学物理学杂志,2021,38(8):971.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.010]
　CAO Yangsen,ZHU Xiaofei,HAN Miaofei,et al.Automatic segmentation of the stomach and pancreas using cascaded deep convolutional neural network[J].Chinese Journal of Medical Physics,2021,38(12):971.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.010]

备注/Memo

备注/Memo:
【收稿日期】2022-06-13 【基金项目】国家自然科学基金（81872718） 【作者简介】王霄，博士研究生，研究方向：流行病与卫生统计，E-mail: naturalconan@163.com 【通信作者】艾自胜，博士，博士生导师，研究方向：流行病与卫生统计，E-mail: azs1966@126.com
更新日期/Last Update: 2022-12-23