现代信息科技2024，Vol.8Issue(16)：28-33,6.DOI:10.19850/j.cnki.2096-4706.2024.16.007
基于轴-Transformer的医学图像分割模型Axial-TransUNet
Axial-TransUNet of Medical Image Segmentation Model Based on Axis-Transformer
摘要
Abstract
A medical image segmentation network Axial-TransUNet based on Axial Attention Mechanism is proposed to address the issues of high computational complexity and insufficient ability to capture positional information in the Transformer Self-Attention Mechanism in TransUNet.On the basis of retaining the TransUNet network encoder,decoder,and skip connections,this network uses residual axial attention blocks based on Axial Attention Mechanism to replace the Transformer layer of TransUNet.The experimental results show that compared to other medical image segmentation networks such as TransUNet,Axial TransUNet performs better in Dice coefficient and intersection union ratio on multiple medical datasets.Compared with TransUNet,the parameter count and FLOPs of the Axial TransUNet network are reduced by 14.9%and 30.5%,respectively.It can be seen that Axial TransUNet effectively reduces model complexity and enhances the model's ability to capture positional information.关键词
医学图像分割/卷积神经网络/位置信息/计算复杂度/轴向注意力机制Key words
medical image segmentation/Convolutional Neural Networks/positional information/computational complexity/Axial Attention Mechanism分类
信息技术与安全科学引用本文复制引用
刘文科,刘琳,韩子逸,张媛媛..基于轴-Transformer的医学图像分割模型Axial-TransUNet[J].现代信息科技,2024,8(16):28-33,6.基金项目
大学生创新训练项目(202310429355) （202310429355）
国家自然科学基金项目(61902430) （61902430）
