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基于多尺度注意力机制的真菌显微图像分类方法

张雪媛 许鸿雁 董跃明 刘丹凤 孙鹏蕊 颜锐 崔洪亮 雷红 任菲

张雪媛, 许鸿雁, 董跃明, 刘丹凤, 孙鹏蕊, 颜锐, 崔洪亮, 雷红, 任菲. 基于多尺度注意力机制的真菌显微图像分类方法[J]. 协和医学杂志, 2023, 14(1): 139-147. doi: 10.12290/xhyxzz.2022-0169
引用本文: 张雪媛, 许鸿雁, 董跃明, 刘丹凤, 孙鹏蕊, 颜锐, 崔洪亮, 雷红, 任菲. 基于多尺度注意力机制的真菌显微图像分类方法[J]. 协和医学杂志, 2023, 14(1): 139-147. doi: 10.12290/xhyxzz.2022-0169
ZHANG Xueyuan, XU Hongyan, DONG Yueming, LIU Danfeng, SUN Pengrui, YAN Rui, CUI Hongliang, LEI Hong, REN Fei. Fungal Microscopic Image Classification Based on Multi-scale Attention Mechanism[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 139-147. doi: 10.12290/xhyxzz.2022-0169
Citation: ZHANG Xueyuan, XU Hongyan, DONG Yueming, LIU Danfeng, SUN Pengrui, YAN Rui, CUI Hongliang, LEI Hong, REN Fei. Fungal Microscopic Image Classification Based on Multi-scale Attention Mechanism[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 139-147. doi: 10.12290/xhyxzz.2022-0169

基于多尺度注意力机制的真菌显微图像分类方法

doi: 10.12290/xhyxzz.2022-0169
基金项目: 

国家重点研发计划 2021YFF1201005

解放军总医院医疗大数据与人工智能研发项目 2019MBD-050

详细信息
    通讯作者:

    雷红, E-mail:jlh309@163.com

    任菲, E-mail:renfei@ict.ac.cn

  • 中图分类号: R756; TP29

Fungal Microscopic Image Classification Based on Multi-scale Attention Mechanism

Funds: 

National Key R & D Program 2021YFF1201005

Medical Big Data and Artificial Intelligence R & D Project of PLA General Hospital 2019MBD-050

More Information
  • 摘要:   目的  利用深度学习技术,建立临床常见的侵袭性真菌图像辅助分类模型。  方法  回顾性收集2020年9月—2021年4月解放军总医院第八医学中心曲霉菌属、酵母菌属和新型隐球菌属真菌感染者的显微镜图像,按7∶1.5∶1.5的比例随机分为训练集、验证集和测试集。使用训练集和验证集图像对改进的MobileNetV2网络结构进行训练和参数调试,构建基于多尺度注意力机制的卷积神经网络(convolutional neural network, CNN)真菌图像11分类模型。以机器鉴定结果为金标准,以查准率、召回率和F1值为指标评价该模型对测试集真菌图像的分类效果。将该模型与5种经典CNN模型进行比较,评价指标包括模型参数量、内存占用量、网络每秒处理的图像数量(frames per second, FPS)、准确率及受试者操作特征曲线下面积(area under the curve,AUC)。  结果  共纳入真菌显微镜图像7666张,分别包括曲霉菌属、酵母菌属和新型隐球菌属图像2781张、4115张、770张。其中训练集5366张、验证集1150张、测试集1150张。改进的MobileNetV2模型对测试集11种真菌图像具有较高的分类性能,查准率为96.36%~100%,召回率为96.53%~100%,F1值为97.01%~100%。该模型的参数量、内存占用量分别为4.22 M、356.89 M,FPS为573,准确率为(99.09±0.18)%,AUC为0.9944±0.0018,综合性能优于5种经典网络模型。  结论  本研究提出的真菌图像分类模型,在保持低运算成本的情况下,可获得较高的真菌图像识别能力,其整体性能优于常见的经典模型。
    作者贡献:张雪媛、许鸿雁负责研究方案设计、实验操作、数据分析与论文撰写;董跃明、刘丹凤、孙鹏蕊负责数据采集与标注;颜锐、崔洪亮、雷红、任菲负责数据审核及内容审校。
    利益冲突:所有作者均声明不存在利益冲突
  • 图  1  去噪前后的真菌图像对比

    图  2  基于改进的MobileNetV2模型结构示意图

    图  3  Dilated SE模块运行示意图

    图  4  11种真菌显微镜图像

    图  5  基于改进的MobileNetV2真菌分类模型混淆矩阵

    图  6  部分真菌原始显微镜图像和类激活热力图

    A.光滑念珠菌;B.葡萄芽菌;C.新型隐球菌;D.黑曲霉菌;E.烟曲霉菌;F.杂色曲霉菌
    (1)原始显微镜图像;(2)类激活热力图,颜色越趋于红色,表示该区域受网络结构的关注度越高

    表  1  改进的MobileNetV2真菌分类模型在测试集中的表现(x±s,%)

    菌种 查准率 召回率 F1值
    烟曲霉菌 99.77±0.16 98.95±0.29 99.36±0.08
    杂色曲霉菌 96.41±0.62 99.77±0.16 98.06±0.25
    黑曲霉菌 100±0 97.16±0.63 98.56±0.32
    解脂假丝酵母菌 99.63±0 100±0 99.81±0
    近平滑念珠菌 96.36±0.51 97.90±0.46 97.12±0.46
    葡萄芽菌 97.50±0.70 96.53±0.17 97.01±0.40
    季也蒙念珠菌 99.87±0.18 99.35±0.49 99.61±0.16
    克柔念珠菌 100±0 100±0 100±0
    光滑念珠菌 99.39±0.86 96.97±1.71 98.15±0.77
    热带念珠菌 96.53±1.87 99.28±1.02 97.86±0.85
    新型隐球菌 100±0 99.57±0 99.78±0
    下载: 导出CSV

    表  2  不同分类模型在测试集中的运行结果

    指标 参数量(M) 内存占用量(M) FPS 准确率(x±s,%) AUC(x±s)
    ResNet18 11.18 252.44 527 98.83±0.16 0.9935±0.0008
    MobileNetV2 2.24 603.82 501 98.68±0.12 0.9928±0.0006
    SENet 0.74 292.24 562 97.78±1.70 0.9878±0.0032
    DenseNet121 6.97 1341.44 177 98.81±0.27 0.9939±0.0011
    Inception V3 21.81 468.05 105 98.84±0.11 0.9948±0.0007
    改进的MobileNetV2 4.22 356.89 573 99.09±0.18 0.9944±0.0018
    FPS:网络每秒处理的图像数量;AUC:曲线下面积
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-31
  • 录用日期:  2022-05-26
  • 网络出版日期:  2022-09-20
  • 刊出日期:  2023-01-30

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