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基于特权信息集成学习的精神分裂症单模态神经影像计算机辅助诊断

沈璐 王倩婷 施俊

沈璐, 王倩婷, 施俊. 基于特权信息集成学习的精神分裂症单模态神经影像计算机辅助诊断[J]. 仁和测试, 2020, 37(3): 405-411, 418. doi: 10.7507/1001-5515.201905029
引用本文: 沈璐, 王倩婷, 施俊. 基于特权信息集成学习的精神分裂症单模态神经影像计算机辅助诊断[J]. 仁和测试, 2020, 37(3): 405-411, 418. doi: 10.7507/1001-5515.201905029
Lu SHEN, Qianting WANG, Jun SHI. Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information[J]. Rhhz Test, 2020, 37(3): 405-411, 418. doi: 10.7507/1001-5515.201905029
Citation: Lu SHEN, Qianting WANG, Jun SHI. Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information[J]. Rhhz Test, 2020, 37(3): 405-411, 418. doi: 10.7507/1001-5515.201905029

基于特权信息集成学习的精神分裂症单模态神经影像计算机辅助诊断

doi: 10.7507/1001-5515.201905029
基金项目: 国家自然科学基金面上项目(61471231);上海市科委“科技创新行动计划”项目(17411953400);上海市科委“科技创新行动计划”地方院校能力建设项目(18010500600)
详细信息
    通讯作者:

    施俊,Email:junshi@shu.edu.cn

Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information

Funds: The National Natural Science Foundation of China
More Information
  • 摘要: 神经影像技术目前已经应用于精神分裂症的诊断。为了提升基于单模态神经影像的精神分裂症计算机辅助诊断(CAD)的性能,本文提出一种基于特权信息学习(LUPI)分类器的集成学习算法。该算法首先对单模态数据采用极限学习机-自编码器(ELM-AE)进行特征二次学习,然后通过随机映射算法将高维特征随机分成多个子空间,并进行两两组合形成源领域和目标领域数据对,用于训练多个支持向量机+(SVM+)弱分类器,最终通过集成学习获得一个强分类器,实现有效的模式分类。本算法在公开的精神分裂症神经影像数据库中进行了实验,包括结构磁共振成像和功能磁共振成像数据。结果表明该算法取得了最优的诊断结果,其在基于结构磁共振成像诊断的分类精度、敏感性和特异性分别可以达到 72.12% ± 8.20%、73.50% ± 15.44% 和 70.93% ± 12.93%,而基于功能磁共振成像诊断的分类精度、敏感性和特异性分别为 72.33% ± 8.95%、68.50% ± 16.58%、75.73% ± 16.10%。本文算法的主要创新点在于克服了传统的 LUPI 分类器需要额外的特权信息模态的不足,可以直接应用于单模态数据分类问题,而且还提升了分类性能,因此具有较为广泛的应用前景。
  • 图  1  结合 ELM-AE 的集成 SVM+分类算法流程图

    Figure  1.  Flowchart of ensemble SVM+ classifier with ELM-AE

    图  2  ELM-AE 的网络结构图

    Figure  2.  Network structure of ELM-AE

    图  3  基于 sMRI 数据不同算法的 ROC 曲线

    Figure  3.  ROC curves of different algorithms based on sMRI data

    图  4  基于 fMRI 数据不同算法的 ROC 曲线

    Figure  4.  ROC curves of different algorithms based on fMRI data

    表  1  基于 sMRI 数据不同算法分类的结果

    Table  1.   Classification results of different algorithms based on sMRI data

    算法 精度(%) 敏感度(%) 特异性(%) AUC
    EA-SVM 69.41 ± 10.25 67.90 ± 16.06 70.92 ± 15.53 0.727 2
    EA-Voting-SVM 70.46 ± 8.38 68.00 ± 16.54 72.53 ± 13.49
    EA-MDO-SVM 70.59 ± 7.16 70.50 ± 17.26 71.24 ± 11.82
    EA-MKB-SVM 70.73 ± 8.43 70.50 ± 21.11 70.75 ± 16.63 0.733 6
    EA-Voting-SVM+ 71.42 ± 7.05 74.00 ± 14.40 70.00 ± 10.87
    EA-MDO-SVM+ 71.69 ± 8.87 72.50 ± 14.43 70.78 ± 12.69
    EA-MKB-SVM+ 72.12 ± 8.20 73.50 ± 15.44 70.93 ± 12.93 0.767 7
    下载: 导出CSV

    表  2  基于 fMRI 数据不同算法分类的结果

    Table  2.   Classification results of different algorithms based on fMRI data

    算法 精度(%) 敏感度(%) 特异性(%) AUC
    EA-SVM 68.42 ± 10.92 67.50 ± 16.57 69.20 ± 15.60 0.707 1
    EA-Voting-SVM 70.01 ± 9.10 64.00 ± 16.66 75.42 ± 17.38
    EA-MDO-SVM 70.96 ± 8.38 64.00 ± 16.66 77.16 ± 16.21
    EA-MKB-SVM 70.95 ± 7.29 65.00 ± 18.33 74.80 ± 14.28 0.728 0
    EA-Voting-SVM+ 71.91 ± 9.68 69.50 ± 16.77 74.09 ± 15.52
    EA-MDO-SVM+ 71.43 ± 9.13 69.50 ± 16.17 73.20 ± 15.61
    EA-MKB-SVM+ 72.33 ± 8.95 68.50 ± 16.58 75.73 ± 16.10 0.735 7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-10
  • 修回日期:  2019-11-27
  • 刊出日期:  2020-03-17

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