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基于双阶段元学习的小样本中医舌色域自适应分类方法

卓力 张雷 贾童瑶 李晓光 张辉

卓力, 张雷, 贾童瑶, 李晓光, 张辉. 基于双阶段元学习的小样本中医舌色域自适应分类方法[J]. 电子与信息学报, 2024, 46(3): 986-994. doi: 10.11999/JEIT230249
引用本文: 卓力, 张雷, 贾童瑶, 李晓光, 张辉. 基于双阶段元学习的小样本中医舌色域自适应分类方法[J]. 电子与信息学报, 2024, 46(3): 986-994. doi: 10.11999/JEIT230249
ZHUO Li, ZHANG Lei, JIA Tongyao, LI Xiaoguang, ZHANG Hui. Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 986-994. doi: 10.11999/JEIT230249
Citation: ZHUO Li, ZHANG Lei, JIA Tongyao, LI Xiaoguang, ZHANG Hui. Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 986-994. doi: 10.11999/JEIT230249

基于双阶段元学习的小样本中医舌色域自适应分类方法

doi: 10.11999/JEIT230249
基金项目: 国家自然科学基金(61871006),国家中医药管理局中医药创新团队及人才支持计划(ZYYCXTD-C-202210)
详细信息
    作者简介:

    卓力:女,教授,博士生导师,研究方向为医学影像处理、机器视觉和多媒体通信

    张雷:男,硕士生,研究方向为人工智能系统设计与集成

    贾童瑶:女,博士生,研究方向为图像增强

    李晓光:男,副教授,硕士生导师,研究方向为医学影像处理和超分辨率图像复原

    张辉:男,副教授,硕士生导师,研究方向为机器视觉和嵌入式系统

    通讯作者:

    卓力 zhuoli@bjut.edu.cn

  • 中图分类号: TN911.73; TP391

Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning

Funds: The National Natural Science Foundation of China (61871006), Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (ZYYCXTD-C-202210)
  • 摘要: 舌色是中医(TCM)望诊最关注的诊察特征之一。在实际应用中,通过一台设备采集到的舌象数据训练得到的舌色分类模型应用于另一台设备时,由于舌象数据分布特性不一致,分类性能往往急剧下降。为此,该文提出一种基于双阶段元学习的小样本中医舌色域自适应分类方法。首先,设计了一种双阶段元学习训练策略,从源域有标注样本中提取域不变特征,并利用目标域的少量有标注数据对网络模型进行微调,使得模型可以快速适应目标域的新样本特性,提高舌色分类模型的泛化能力并克服过拟合。接下来,提出了一种渐进高质量伪标签生成方法,利用训练好的模型对目标域的未标注样本进行预测,从中挑选出置信度高的预测结果作为伪标签,逐步生成高质量的伪标签。最后,利用这些高质量的伪标签,结合目标域的有标注数据对模型进行训练,得到舌色分类模型。考虑到伪标签中含有噪声问题,采用了对比正则化函数,可以有效抑制噪声样本在训练过程中产生的负面影响,提升目标域舌色分类准确率。在两个自建中医舌色分类数据集上的实验结果表明,在目标域仅提供20张有标注样本的情况下,舌色分类准确率达到了91.3%,与目标域有监督的分类性能仅差2.05%。
  • 图  1  双阶段元学习的中医舌色域自适应分类整体框图

    图  2  渐进高质量伪标签生成过程

    图  3  两个自建数据集中的部分样本示例

    表  1  两个自建数据集的样本类别和数量

    数据集类别和数量总数
    淡红暗红暗紫
    SIPL-A13013011436410
    SIPL-B20019011035535
    下载: 导出CSV

    表  2  采用有监督学习方法得到的两个数据集分类精度(%)

    淡红舌红舌暗红舌暗紫舌整体精度
    SIPL-A91.2592.4896.80100.0094.03
    SIPL-B93.9093.1995.6585.7193.35
    下载: 导出CSV

    表  3  不同训练策略对伪标签准确率的影响(%)

    训练策略伪标签准确率
    策略166.5
    策略276.8
    策略374.8
    策略479.9
    策略582.0
    下载: 导出CSV

    表  4  不同目标域有标注样本数量对伪标签准确率的影响(%)

    目标域有标注样本训练策略伪标签准确率
    1例双阶段元学习78.6
    3例双阶段元学习80.7
    5例双阶段元学习82.0
    下载: 导出CSV

    表  5  不同元学习方法对伪标签准确率的影响(%)

    元学习方法单阶段元学习
    伪标签准确率
    双阶段元学习
    伪标签准确率
    Matching networks77.278.5
    Relation network77.879.8
    SNAML78.581.2
    Online MAML79.581.7
    本文方法79.982.0
    下载: 导出CSV

    表  6  不同伪标签生成方法的结果对比

    方法伪标签数量伪标签
    准确率 (%)
    一次性伪标签生成/82.00
    一次性伪标签生成+模型集成/83.96
    渐进伪标签生成+置信度阈值20185.50
    渐进伪标签生成+置信度阈值+模型集成14687.00
    下载: 导出CSV

    表  7  渐进高质量伪标签生成的轮次结果对比

    方法高质量伪标签伪标签准确率 (%)
    第1轮14687.00
    第2轮14088.10
    第3轮14588.19
    第4轮14888.16
    下载: 导出CSV

    表  8  不同有噪样本学习方法的分类结果对比(%)

    方法基于最优伪标签+少量标注的分类准确率
    直接训练87.50
    GCE90.01
    PENCIL87.76
    SCE90.25
    Co-teaching89.86
    Co-teaching+89.98
    CTR90.35
    CTR+GCE90.80
    本文方法(CTR+SCE)91.30
    下载: 导出CSV

    表  9  不同域自适应方法的结果对比(%)

    方法分类准确率
    MME82.6
    CDAC83.8
    Adamatch89.1
    本文方法91.3
    下载: 导出CSV

    表  10  消融实验(%)

    ResNet18双阶段元
    学习
    渐进高质量
    伪标签生成
    CTRR+SCE分类
    准确率
    76.8
    82.3
    87.5
    91.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-11
  • 修回日期:  2023-09-07
  • 网络出版日期:  2023-09-12
  • 刊出日期:  2024-03-27

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