集成自注意力机制的医学图像分割方法OA北大核心CSTPCD
Medical Image Segmentation Method with Integrated Self-attention
针对UNet架构在医学图像分割中捕捉局部特征及保留边缘细节的局限性,提出了一种融合自注意力机制的改进型UNet算法.该算法基于传统编码-解码结构,引入多尺度卷积(Multi-scale convolution,MSC)模块以实现多粒度特征提取,同时集成卷积-自注意力(Convolution mixer attention,CMA)模块,结合卷积层的局部特征建模和自注意力层的全局上下文建模.在BUSI和DDTI数据集分割任务中,相比现有经典网络架构…查看全部>>
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 lo…查看全部>>
赵凡;张学典
上海理工大学光电信息与计算机工程学院,上海 200093上海理工大学光电信息与计算机工程学院,上海 200093
计算机与自动化
UNet医学图像分割卷积神经网络多尺度卷积注意力机制
UNetmedical image segmentationconvolutional neural network(CNN)multi-scale convolution(MSC)attention mechanism
《数据采集与处理》 2024 (5)
1240-1250,11
国家重点研发计划资助项目(2021YFB2802300).
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