文摘
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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
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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
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电子学报
,2022,50(10):2433-2442 【核心库】
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DOI
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10.12263/DZXB.20200984
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关键词
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自注意力
;
分解卷积
;
医学图像分割
;
深度学习
;
U-Net
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地址
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青岛科技大学信息科学技术学院, 山东, 青岛, 266061
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语种
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中文 |
文献类型
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研究性论文 |
ISSN
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0372-2112 |
学科
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自动化技术、计算机技术 |
基金
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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
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文献收藏号
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CSCD:7318662
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