[1]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):1113-1118.[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(9):1113-1118.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.022]
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 深度神经网络技术在肿瘤细胞识别中的应用()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第9期
页码:
1113-1118
栏目:
其他（激光医学等）
出版日期:
2019-09-25

文章信息/Info

Title:
 Application of deep neural network in tumor cell recognition
文章编号:
1005-202X（2019）09-1113-06
作者:
 纪春阳; 徐秀林; 王燕
 上海理工大学医疗器械与食品学院， 上海 200093
Author(s):
 JI Chunyang;  XU Xiulin;  WANG Yan
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
关键词:
 ; 深度神经网络; 卷积神经网络; 人工智能; 肿瘤细胞; 综述
Keywords:
 ; deep neural network;  convolutional neural network;  artificial intelligence;  tumor cell;  review
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.09.022
文献标志码:
A
摘要:
 深度神经网络（DNN）作为人工智能最主要的分支，是基于模仿人脑思考方式的计算机程序，旨在模拟人类大脑处理信息的方式对事物进行分类或预测。DNN的通用性表现为：自我学习、自适应、联想记忆，即使没有先验背景也可以执行各种任务。近年来DNN受到国内外医学界的广泛重视，尤其在精准分类肿瘤细胞数字图像的自动识别方面已经取得了重大突破，DNN通过强化学习并因此获得经验，使医生能够向患者提供准确的诊疗方案。本文主要综述了DNN技术在肿瘤细胞识别的最新研究进展，详细阐述卷积神经网络、深度信念网络、生成对抗网络、深度残差网络的原理及其应用实例，比较基于不同模型的神经网络，对各类模型在应用层面上的精准度和性能进行分析，提出DNN在肿瘤细胞识别领域中面临的问题及未来的发展趋势。
Abstract:
 Abstract: Deep neural network (DNN), as the main branch of artificial intelligence, is a computer program based on the imitation of the way of how human brain thinks, aiming to simulate the way the human brain processes information for classifying or predicting things. The universality of DNN includes self-learning, self-adaptation and associative memory. DNN can perform various tasks even without a priori background. In recent years, DNN has received extensive attention from domestic and international medical communities. Moreover, some major breakthroughs have been made in accurately classifying the automatic recognition of digital images of tumor cells. DNN gains experience through intensive learning, which enables doctors to provide patients with an accurate treatment strategy. Herein the latest research progress of DNN in tumor cell recognition is reviewed, and the principles of convolutional neural network, deep belief network, generative adversarial network and deep residual network as well as their applications are elaborated. The neural networks based on different models are compared, and the accuracy and performance of various models in application are analyzed. Finally, the problems and future development trends of DNN in tumor cell recognition are pointed out.

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备注/Memo

备注/Memo:
 【收稿日期】2019-02-11
【基金项目】上海市科委科技支撑计划（19441904500）
【作者简介】纪春阳，硕士研究生，研究方向：精密医疗器械，E-mail:cyangji0830@163.com
【通信作者】徐秀林，教授，研究方向：医疗仪器的开发及其检测技术，E-mail: xxlin100@163.com
更新日期/Last Update: 2019-09-24