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LRSAR-Net语义分割模型用于新冠肺炎CT图片辅助诊断

张桃红 郭徐徐 张颖

张桃红, 郭徐徐, 张颖. LRSAR-Net语义分割模型用于新冠肺炎CT图片辅助诊断[J]. 电子与信息学报, 2022, 44(1): 48-58. doi: 10.11999/JEIT210917
引用本文: 张桃红, 郭徐徐, 张颖. LRSAR-Net语义分割模型用于新冠肺炎CT图片辅助诊断[J]. 电子与信息学报, 2022, 44(1): 48-58. doi: 10.11999/JEIT210917
ZHANG Taohong, GUO Xuxu, ZHANG Ying. LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image[J]. Journal of Electronics & Information Technology, 2022, 44(1): 48-58. doi: 10.11999/JEIT210917
Citation: ZHANG Taohong, GUO Xuxu, ZHANG Ying. LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image[J]. Journal of Electronics & Information Technology, 2022, 44(1): 48-58. doi: 10.11999/JEIT210917

LRSAR-Net语义分割模型用于新冠肺炎CT图片辅助诊断

doi: 10.11999/JEIT210917
基金项目: 科技部国家重点研发计划(2018YFC1707410)
详细信息
    作者简介:

    张桃红:女,1973年生,博士,副教授,研究方向为机器视觉、深度学习

    郭徐徐:男,1996年生,硕士生,研究方向为机器视觉、深度学习

    张颖:女,1986年生,硕士,研究方向为机器视觉、深度学习

    通讯作者:

    张桃红 zth_ustb@163.com

  • 中图分类号: TN911.73; TP391.41

LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image

Funds: The National Key Research and Development Program of China (2018YFC1707410)
  • 摘要: 自2019年末新型冠状病毒(Covid-19)疫情在全球爆发以来,世界各国都处于疫情的危害之下。新冠病毒通过入侵人体的呼吸系统,造成肺部感染,甚至死亡。CT(Computed Tomography)图是医生对肺炎患者进行诊断的常规方法。为了提高医生对新冠感染者进行诊断的效率,该文提出一种基于低秩张量自注意力重构的语义分割网络LRSAR-Net,其中低秩张量自注意力重构模块用来获取长范围的信息。低秩张量自注意力重构模块主要包括:低秩张量生成子模块、低秩自注意力子模块、高秩张量重构子模块3个部分。低秩张量自注意力模块先生成多个低秩张量,构建低秩自注意力特征图,然后将多个低秩张量注意力特征图重构成高秩注意力特征图。自注意力模块通过计算相似度矩阵来获取长范围的语义信息。与传统的自注意力模块Non-Local相比,低秩张量自注意力重构模块计算复杂度更低,计算速度更快。最后,该文与其他优秀的语义分割模型进行了对比,体现了模型的有效性。
  • 图  1  LRSAR-Net网络结构图

    图  2  解码层的上采样结构和通道注意力

    图  3  低秩张量生成子模块

    图  4  低秩自注意力子模块

    图  5  高秩张量重构子模块

    图  6  Covid-19患者肺部CT图片

    图  7  ED Net训练过程中的准确率Acc、平均交并比mIoU和损失的变化

    图  8  LRSAR-Net训练过程中的准确率Acc、平均交并比mIoU和损失的变化

    图  9  实验分割结果

    表  1  不同的特征提取网络的模型对比DataSet

    数据集图片数量Covid-19 数量
    Covid-19 CT100100100
    Covid-19 P9829373
    下载: 导出CSV

    表  2  注意力模块的影响

    模型Train_mIoU(%)Train_Acc(%)Test_mIoU(%)Test_Acc(%)参数量(M)FLOPs(G)
    ED Net73.796.965.494.732.5110.59
    +Non-Local72.496.967.094.6+34.27+2.56
    +LRSAR74.097.069.095.0+17.13+1.28
    +SE74.997.069.195.3+1.3+0.02
    Reco Net[18]73.496.967.594.5113.8516.35
    LRSAR-Net73.797.170.095.150.9411.88
    下载: 导出CSV

    表  3  不同的特征提取网络的模型对比

    模型Train_mIoU(%)Train_Acc(%)Test_mIoU(%)Test_Acc(%)参数量(M)FLOPs(G)
    ResNet5074.797.270.095.150.9411.88
    MobileNetV2[27]74.096.968.094.925.064.68
    InceptionV4[28]75.897.170.995.567.2216.67
    Xception[29]75.697.169.095.247.2011.86
    下载: 导出CSV

    表  4  不同的语义分割网络之间的对比

    模型Train_mIoU(%)Train_Acc(%)Test_mIoU(%)Test_Acc(%)参数量(M)FLOPs(G)
    U-Net[30]73.796.965.494.732.510.6
    U-Net++[31]73.796.766.194.948.957.4
    DeepLabV3[32]71.696.566.094.139.640.8
    DeepLabV3+[33]73.397.165.794.6269.1
    PSPNet[34]71.696.767.394.32.22.8
    Reco-Net[18]73.496.967.594.5113.8516.35
    LRSAR-Net73.796.870.095.134.210.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-01
  • 修回日期:  2022-12-01
  • 录用日期:  2021-12-24
  • 网络出版日期:  2021-12-30
  • 刊出日期:  2022-01-10

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