基于深度学习的胃癌病理图像分类方法  

Pathological Image Classification of Gastric Cancer Based on Depth Learning

作　　者：张泽中 高敬阳[1] 吕纲 赵地 ZHANG Ze-zhong;GAO Jing-yang;LV Gang;ZHAO Di(College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China;National Research Center of Translational Medicine(Shanghai),Shanghai 200025,China;Chinese Human Genome Center at Shanghai,Shanghai 201203,China;The Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]北京化工大学信息科学与技术学院,北京100029 [2]国家转化医学中心(上海),上海2000252 [3]国家人类基因组南方中心,上海201203 [4]中国科学院计算技术研究所,北京100190

出　　处：《计算机科学》2018年第B11期263-268,共6页Computer Science

基　　金：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);北京市自然科学基金资助项目(5182018)资助;北京市科技计划项目(Z171100000117001);北京市科技计划项目(Z161100000216143)

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。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.

关 键 词：深度学习 卷积神经网络 胃癌病理图像 GoogLeNet优化 模型融合 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]