摘要
为了提高特纳综合征(Turner Syndrome, TS)的诊断效率,提出一种基于小样本学习和多尺度残差网络的TS预测模型。对TS人脸图像进行预处理获取人脸主要区域,提出具有多级注意力机制的多尺度残差模块,其中,多尺度残差模块以集成多尺寸卷积核的残差结构实现,多级注意力机制用来学习特征通道关系和不同卷积核的重要性,利用该模块构建多尺度残差网络。使用小样本学习进行模型训练。实验结果表明,该模型能够提升TS的诊断准确率。
A prediction model is proposed for improving the diagnosis efficiency of Turner syndrome(TS)based on a multiscale residual network(MRN)and few-shot learning.TS facial images were pre-processed to obtain the main facial areas.A multiscale residual block(MRB)with multilevel attention mechanisms(MAM)was designed.The MRB was implemented by integrating the residual structure of multi-scale convolution kernels,and the MAM was used to learn feature channel relationships and the importance of different convolution kernels.The MRN was built using the MRB.The few-shot learning was utilized to train the MRN.The experimental results demonstrate that the prediction model can improve the diagnostic accuracy of TS.
作者
刘璐
Liu Lu(School of Software Engineering,Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处
《计算机应用与软件》
北大核心
2024年第9期182-189,共8页
Computer Applications and Software
基金
国家重点研发计划项目(2020YFB2104402)。
关键词
特纳综合征
注意力机制
残差网络
小样本学习
Turner syndrome
Attention mechanisms
Residual network
Few-shot learning
