摘要
考虑到人工对胃癌病理图像的判别和诊断可能存在漏检的问题,为使诊断更加准确,提出一种基于ResNet和UNet的病理图像诊断系统,旨在实现对病理图像的分类、分割以及输出诊断结果.采用ResNet模型对胃癌病理图像进行有癌和无癌的分类.对UNet模型进行改进,改进后的模型在每个下采样和上采样之前加入卷积注意力模块,以增强模型对癌变区域的关注.使用残差模块替代编码部分的2次卷积,来提高特征的利用率;利用Inception模块来替代解码部分上采样中的2个卷积,从而扩充其宽度并获取不同尺度的特征.将分类与分割结果综合考虑,获取最终的胃癌病理图像的诊断结果.实验结果表明,该系统可以有效地诊断胃癌病理图像中是否存在癌变.
Considering that manual identification and diagnosis of gastric cancer pathological images may cause missed detection and in order to make diagnosis more accurate,a pathological image diagnosis system based on ResNet and UNet is proposed,aiming to classify,segment and output the diagnosis results of pathological images.The ResNet model is used to classify gastric cancer pathological images with and without cancer.The UNet model is improved,and the improved model adds a convolutional block attention module before each down-sampling and up-sampling to enhance the model s attention to cancerous areas.The residual module is used to replace the two convolutions in the encoding part to improve feature utilization;and the Inception module is used to replace the two convolutions in the up-sampling of the decoding part,thereby expanding its width to obtain features of different scales.The classification and segmentation results are comprehensively considered to obtain the final diagnostic results of gastric cancer pathological images.Experimental results show that this system can effectively diagnose the presence of cancer in gastric cancer pathological images.
作者
张文悦
贾子彦
李青
张大川
潘玲佼
沈大伟
ZHANG Wenyue;JIA Ziyan;LI Qing;ZHANG Dachuan;PAN Lingjiao;SHEN Dawei(School of Electrical and Information Engineering,Jiangsu University of Technology,Changzhou 213001,China;Department of Pathology,Changzhou First People s Hospital,Changzhou 213004,China)
出处
《河北大学学报（自然科学版）》
CAS
北大核心
2024年第4期433-440,共8页
Journal of Hebei University(Natural Science Edition)
基金
国家自然科学基金资助项目(62001196)
江苏省“333高层次人才培养工程”项目(2022-3-4-107)
常州市科技计划项目(CM20223015)
常州应用基础研究项目(CJ20220064
CJ20220059)。
关键词
病理图像
图像分类
UNet
图像分割
胃癌诊断
pathological images
image classification
UNet
image segmentation
diagnosis of gastric cancer
