面向WSI的乳腺病理亚型分类研究  

Research of breast pathological subtype classification on WSI

作　　者：陈金令 李洁 赵成明 刘鑫 Chen Jinling;Li Jie;Zhao Chengming;Liu Xin(School of Electrical Information,Southwest Petroleum University,Chengdu 610599,China)

机构地区：[1]西南石油大学电气信息学院,成都610599

出　　处：《计算机应用研究》2022年第10期3167-3173,共7页Application Research of Computers

基　　金：成都市科技厅创新创业资助项目(2018YF0500893GX)。

摘　　要：为实现乳腺病理WSI图像的精准分类,提出了一种基于混合连接的门控卷积神经网络分类方法。搭建了局部残差连接和全局稠密连接的混合模块,将压缩激活门控单元嵌入混合模块,建立了混合模块与过渡层交替连接的骨干网络。结合基于四叉树分割的图像数据增强方法训练模型,基于BreastSet临床数据集的实验结果得出,该方法的图像级、患者级和病理级准确率分别达到92.24%、92.83%和92.18%,相较其他方法,其准确率提高,参数量和计算量降低,更具有临床应用价值。For precise classification of pathological breast WSI images,this paper proposed the gated convolution network based on hybrid connection(HC-GCN),set up a hybrid block of local residual connection and global dense connection,and through embedding the squeeze-excitation-and-gated(SEG)module into the hybrid block,established a backbone network for alternate connection between the hybrid block and the transition layer.In combination with a training model for image data enhancement based on quad-tree image segmentation method,the experimental results based on the BreastSet clinical data set show 92.24%,92.83%and 92.18%of accuracy at the image level,patient level and pathology level respectively.Therefore,compared with other methods,this method has great clinical application value for improving accuracy as well as reducing number of parameters and amount of computation.

关 键 词：全切片图像 乳腺病理亚型分类 计算机辅助诊断 门控卷积网络 混合连接 

分 类 号：TP391.5[自动化与计算机技术—计算机应用技术]