数据采集与处理2024，Vol.39Issue(5)：1240-1250,11.DOI:10.16337/j.1004-9037.2024.05.015
集成自注意力机制的医学图像分割方法
Medical Image Segmentation Method with Integrated Self-attention
摘要
Abstract
Aiming at the limitations of the UNet architecture in capturing local features and preserving edge details in medical image segmentation,this paper presents an improved UNet algorithm integrating self-attention mechanism.The proposed algorithm is based on traditional encoder-decoder structure,incorporating a multi-scale convolution(MSC)block for multi-granularity feature extraction,and a convolution mixer attention(CMA)block,which combines the modeling of local features by convolutional layers with global contextual modeling by self-attention layers.In the segmentation task of BUSI and DDTI datasets,compared with the existing classical network architecture,a large number of experimental data verify the excellent segmentation ability of the model.Additionally,Statistical data analysis and ablation studies further confirm the effectiveness of the MSC and CMA modules.This research provides an innovative approach for high-precision medical image segmentation,holding significant theoretical and practical implications for enhancing the accuracy and efficiency of medical diagnoses.关键词
UNet/医学图像分割/卷积神经网络/多尺度卷积/注意力机制Key words
UNet/medical image segmentation/convolutional neural network(CNN)/multi-scale convolution(MSC)/attention mechanism分类
信息技术与安全科学引用本文复制引用
赵凡,张学典..集成自注意力机制的医学图像分割方法[J].数据采集与处理,2024,39(5):1240-1250,11.基金项目
国家重点研发计划资助项目(2021YFB2802300). （2021YFB2802300）
