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基于度量学习的多分支舌象识别网络

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  • NETL
  • NSTL
  • 万方数据
  • 维普
为提升舌象识别效率与精准度，通过度量学习研究辅助医生识别舌象表征的方法。首先，收集舌诊图像111例，数据按照7:3的比例随机分为训练集和测试集。然后，设计一种基于度量学习的多分支舌象识别网络。深度学习网络被分为两个部分，前半部分为共享权重层，采用基于度量学习的舌象特征编码损失函数，以获得精准的特征;后半部分针对中医舌象的分类分为4个舌象识别辅助分支，降低舌象识别难度，提升准确率。此外，构建多标签残差映射，增加类间距，减小类内距，提升最终识别的准确度。本文方法在舌象数据集的测试集上进行测试时获得84。8%的识别精度，表明多分支网络架构可以很好地降低舌象识别难度，特别是特征类别较多的舌形和苔质。同时，舌象特征编码损失函数可以有效地提取舌象特征;舌象多标签残差映射可以减少各类别之间的干扰，从而提升识别准确度。
Metric learning based multi-branch network for tongue manifestation recognition
Based on metric learning,a novel method of assisting doctors in identifying tongue manifestation is proposed to improve the efficiency and accuracy of tongue manifestation recognition.A total of 111 tongue images are collected,and the data are randomly divided into training set and test set at a ratio of 7:3.Subsequently,a metric learning based multi-branch tongue manifestation recognition network is designed.The deep learning network is divided into 2 parts.The first part is the shared weight layer which employs metric learning based loss function in tongue manifestation feature coding to obtain accurate features.In order to reduce the difficulty of tongue manifestation recognition and improve the accuracy,the latter part is split into 4 branches for tongue manifestation recognition which correspond to the classification of tongue manifestation in traditional Chinese medicine.Additionally,a multi-label residual mapping is constructed to increase inter-class distance and reduce intra-class distance,so as to enhance the accuracy of final recognition.The proposed method achieves a recognition accuracy of 84.8%on the test set of tongue manifestation dataset,indicating that multi-branch network architecture can lower the difficulties in tongue manifestation recognition,especially for the tongue shape and coating nature with multiple feature categories.The loss function in tongue manifestation feature coding can effectively extract tongue features,while multi-label residual mapping can reduce the interference between different categories,which improves the recognition accuracy.

tongue manifestation recognitionmulti-branch network architecturefeature codingloss functionmulti-label residual mapping

任思羽、吴瑞、罗庆林、肖开慧、王艺凡、利节

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成都开放大学教学部,四川成都 610000

重庆科技学院智能技术与工程学院,重庆 401331

重庆科瑞制药有限公司,重庆 400060

首都医科大学附属北京友谊医院中医科,北京 100050

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舌象识别 多分支网络架构 特征编码 损失函数 多标签残差映射

国家科技部"科技助力经济"重点专项(2020)重庆市自然科学基金重庆市教委科学技术研究计划重庆科技学院硕士研究生创新计划

SQ2020YFF0405970cstc2020jcyjmsxmX0683KJQN201901507ZNYKJCX2022023

2024

10.3969/j.issn.1005-202X.2024.04.018

中国医学物理学杂志
南方医科大学,中国医学物理学会

中国医学物理学杂志

CSTPCD
影响因子：0.483
ISSN：1005-202X
年,卷(期)：2024.41(4)
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