计算机应用与软件2025，Vol.42Issue(6)：241-248,289,9.DOI:10.3969/j.issn.1000-386x.2025.06.031
基于金字塔卷积和像素注意力的分割方法
SEGMENTATION METHOD BASED ON PYRAMID CONVOLUTION AND PIXEL ATTENTION
摘要
Abstract
To address the problems of large variation in size and complex structure of segmentation targets and poor learning of target edge details by neural networks in medical image segmentation tasks,we propose a pyramidal dilated convolution and pixel-level attention network(DP-Net)based on the U-Net network.The dilated convolution pyramid module was constructed and designed to replace the traditional convolution operation,which extended the network perceptual field and encoded the global contextual information through the combination of multiple dilated convolutions.A pixel-level attention module was proposed to further encode inter-pixel dependencies based on the channel attention mechanism enabling the network to learn richer local contextual information from the features of different channels.Through experimental evaluation on the open lung dataset LIDC and private liver tumor dataset,the proposed DP-Net obtains better performance than current methods on all three kind of evaluation metrics,demonstrating the effectiveness of the proposed network improvement in terms of segmentation accuracy.关键词
深度学习/医学图像处理/图像分割/注意力机制/空洞卷积Key words
Deep learning/Medical image processing/Image segmentation/Attention mechanism/Dilated convolution分类
计算机与自动化引用本文复制引用
阴桂梅,肖易勇,席鑫华,赵艳丽,谭淑平,强彦,罗士朝..基于金字塔卷积和像素注意力的分割方法[J].计算机应用与软件,2025,42(6):241-248,289,9.基金项目
国家自然科学基金项目(61872261). （61872261）
