Axial-TransUNet of Medical Image Segmentation Model Based on Axis-Transformer
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.
medical image segmentationConvolutional Neural Networkspositional informationcomputational complexityAxial Attention Mechanism
国家科技期刊平台
NSTL
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