SAU-Net:基于U-Net和自注意力机制的医学图像分割方法
SAU-Net:Medical Image Segmentation Method Based on U-Net and Self-Attention

张淑军彭中李辉

文摘	基于深度学习的生物医学图像分割由于其精度的提高,可以更好地辅助医生做精确的诊断.目前主流的基于U-Net的分割模型通过多层卷积进行局部特征的提取,缺失了全局信息,使分割过于局部化而产生误差.本文通过自注意力机制和分解卷积策略对U-Net模型进行改进,提出一种新的深度分割网络SAU-Net,使用自注意力模块增加全局信息,将原U-Net中的级联结构改为逐像素相加,减小维度,降低计算量;提出一种快速简洁的分解卷积方法,将传统卷积分解为两路一维卷积,并加入残差连接强化上下文信息.在BRATS和Kaggle两个脑肿瘤数据集上进行的实验结果表明,SAU-Net在参数量和Dice系数上都有更优的性能.
其他语种文摘	Biomedical image segmentation based on deep learning can better help doctors make an accurate diagnosis due to its enhanced accuracy.At present,the U-Net-based mainstream segmentation model extracts local features through multi-layer convolutions,which lacks global information and leads to over-localized results with errors.This paper improves the U-Net model through the self-attention mechanism and decomposition convolution and proposes a new deep segmentation network called SAU-Net.The model uses the self-attention module to increase global information,and changes the cascade structure in the original U-Net to pixel-by-pixel addition in order to reduce the dimension and cut down the calculation cost.A fast and concise decomposition convolution method is proposed which integrates the traditional convolution into a two-way one-dimensional convolution,and the residual connection is added to enhance the context information.The experimental results conducted on the two brain tumor datasets of BRATS and Kaggle show that SAU-Net has better performance in terms of parameters and the Dice coefficients.
来源	电子学报 ,2022,50(10):2433-2442 【核心库】
DOI	10.12263/DZXB.20200984
关键词	自注意力 ; 分解卷积 ; 医学图像分割 ; 深度学习 ; U-Net
地址	青岛科技大学信息科学技术学院, 山东, 青岛, 266061
语种	中文
文献类型	研究性论文
ISSN	0372-2112
学科	自动化技术、计算机技术
基金	山东省高等学校青创人才引育计划"人工智能与医学影像分析创新团队"建设项目
文献收藏号	CSCD:7318662