基于深度学习的胃癌病理图像分类方法

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 263-268.

基于深度学习的胃癌病理图像分类方法

张泽中¹, 高敬阳¹, 吕纲^2,3, 赵地⁴

北京化工大学信息科学与技术学院北京100029¹
国家转化医学中心上海上海200025²
国家人类基因组南方中心上海201203³
中国科学院计算技术研究所北京100190⁴

出版日期:2019-02-26 发布日期:2019-02-26
通讯作者: 高敬阳(1966－),女,博士,教授,CCF高级会员,主要研究方向为机器学习、生物信息学,E-mail:gaojy@mail.buct.edu.cn
作者简介:张泽中(1994-),男,硕士生,CCF会员,主要研究方向为深度学习、医学图像数据分析;吕纲(1976－),教授,主要研究方向为机器学习,E-mail:lug@chgc.sh.cn;赵地(1978－),男,博士,副教授,CCF会员,主要研究方向为类脑计算,E-mail:zhaodi@escience.cn。
基金资助:
本文受国家自然科学基金(61472026),国家重点研究发展计划(SQ2017ZX106047),北京市自然科学基金重点项目(4161004),北京市科技计划项目(Z171100000117001),北京市科技计划项目(Z161100000216143),北京市自然科学基金资助项目(5182018)资助。

Pathological Image Classification of Gastric Cancer Based on Depth Learning

ZHANG Ze-zhong¹, GAO Jing-yang¹, LV Gang^2,3, ZHAO Di⁴

College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China¹
National Research Center of Translational MedicineShanghai,Shanghai 200025,China²
Chinese Human Genome Center at Shanghai,Shanghai 201203,China³
The Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China⁴

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

摘要： 针对深度卷积神经网络能够有效提取图像深层特征的能力,选择在图像分类工作中表现优异的GoogLeNet和AlexNet模型对胃癌病理图像进行诊断。针对医学病理图像的特点,对GoogLeNet模型进行了优化,在保证诊断准确率的前提下降低了计算成本。在此基础上,提出模型融合的思想,通过综合不同结构和不同深度的网络模型,来学习更多的图像特征,以获取更有效的胃癌病理信息。实验结果表明,相比原始模型,多种结构的融合模型在胃癌病理图像的诊断上取得了更好的效果。

关键词: GoogLeNet优化, 卷积神经网络, 模型融合, 深度学习, 胃癌病理图像

Abstract: Due to that CNN can effectively extract deep features of the image,this paper used GoogLeNet and AlexNet models which have excellent performance in image classification to diagnose the pathological image of gastric cancer.Firstly,according to the characteristics of medical pathological images,this paper optimized the GoogLeNet model to reduce the computational cost under the premise of ensuring the accuracy of diagnosis.On this basis,it proposed the idea of model fusion.By combining more images with different structures and different depths,more effective pathological information of gastric cancer can be acquired.The experimental results show that the fusion model with multiple structures has achieved better results than the original model in the diagnosis of pathological images for gastric cancer.

Key words: CNN, Deep learning, Gastric cancer pathology, GoogLeNet optimization, Model fusion

中图分类号:

TP391.7

张泽中, 高敬阳, 吕纲, 赵地. 基于深度学习的胃癌病理图像分类方法[J]. 计算机科学, 2018, 45(11A): 263-268. https://doi.org/

ZHANG Ze-zhong, GAO Jing-yang, LV Gang, ZHAO Di. Pathological Image Classification of Gastric Cancer Based on Depth Learning[J]. Computer Science, 2018, 45(11A): 263-268. https://doi.org/

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