电子科技2026，Vol.39Issue(1)：9-17,9.DOI:10.16180/j.cnki.issn1007-7820.2026.01.002
基于空间通道自适应特征的肝脏病理图像分割网络
Segmentation Network Based on Spatial-Channel Adaptive Features for Liver Pathological Image
摘要
Abstract
In view of the problems such as high similarity between the lesion area and the surrounding tissues,low contrast,and blurred boundaries in liver pathological images,this study proposes a liver pathological segmentation network based on spatially and channel-adaptive features.Through the hybrid calibration attention mechanism,the network can adaptively select the feature information calibrated in both spatial and channel dimensions,which is bene-ficial for the encoder to capture the important features related to liver lesions.Additionally,the atrous spatial pyramid pooling module is introduced at the deepest layer of the encoder to compensate for the missing multi-scale information in high-level features,thereby improving the segmentation accuracy of the model.Comparative experiments and abla-tion experiments are conducted on a private liver dataset,a public liver dataset,and two other public pathological data-sets for the proposed network.The experimental results show that,compared with other methods,the segmentation re-sults of the proposed network are better,and it effectively solves the problem of hepatocellular carcinoma segmentation.关键词
肝细胞癌/病理图像/编解码架构/混合校准注意力模块/空间注意力/通道注意力/空洞空间金字塔池化模块/多尺度信息Key words
hepatocellular carcinoma/pathological image/encoder-decoder architecture/hybrid calibration atten-tion block/spatial attention/channel attention/atrous spatial pyramid pooling module/multi-scale information分类
信息技术与安全科学引用本文复制引用
王建宇,王朝立,孙占全,刘晓虹..基于空间通道自适应特征的肝脏病理图像分割网络[J].电子科技,2026,39(1):9-17,9.基金项目
国家自然科学基金(6217323) （6217323）
国防科工局基础研究项目(JCKY2019413D001)National Natural Science Foundation of China(6217323) （JCKY2019413D001）
Basic Research Project of State Administration of Science,Technology and Industry for National Defense(JCKY2019413D001) （JCKY2019413D001）
