[1]杨婕,高阳,段郑玉,等.基于改进CycleGAN网络的面部腧穴定位算法[J].智能系统学报,2025,20(4):1024-1032.[doi:10.11992/tis.202410009]
YANG Jie,GAO Yang,DUAN Zhengyu,et al.Facial acupoint localization algorithm based on the improved CycleGAN[J].CAAI Transactions on Intelligent Systems,2025,20(4):1024-1032.[doi:10.11992/tis.202410009]
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YANG Jie,GAO Yang,DUAN Zhengyu,et al.Facial acupoint localization algorithm based on the improved CycleGAN[J].CAAI Transactions on Intelligent Systems,2025,20(4):1024-1032.[doi:10.11992/tis.202410009]
基于改进CycleGAN网络的面部腧穴定位算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第4期
页码:
1024-1032
栏目:
学术论文—机器学习
出版日期:
2025-08-05
- Title:
- Facial acupoint localization algorithm based on the improved CycleGAN
- Keywords:
- acupuncture; facial acupoint; automatic localization; cycle-consistent adversarial networks; generator; multiscale discriminator; alternating iteration; intelligent traditional Chinese medicine
- 分类号:
- TP391.7
- 文献标志码:
- 2025-5-28
- 摘要:
- 现有腧穴自动定位方法存在定位误差大、算法泛化能力弱、操作复杂等缺点，不能满足大规模针灸临床应用的需求。针对以上问题，提出一种适用于面部腧穴定位的改进循环一致生成对抗网络。采用双循环对抗训练机制，通过对称生成对抗网络的交替迭代实现网络性能优化；针对面部图像的特点，设计内嵌腧穴信息感知块的对称编解码生成器和能够在不同感受野下处理特征的多尺度分块判别器；采用多个损失函数对腧穴定位网络进行约束。实验结果表明，所提算法可实现与人工定位视觉效果相似的结果，为面部腧穴智能定位技术的研究提供全新的视野。
- Abstract:
- The existing methods for automatic acupoint localization suffer from significant positioning errors, poor algorithm generalization, and operational complexity, making them insufficient for large-scale clinical applications in traditional Chinese medicine (TCM) acupuncture. Hence, an improved cycle-consistent generative adversarial network is proposed to address the issue of acupoint localization in TCM acupuncture. A dual-loop adversarial training mechanism is adopted to optimize network performance through alternating iterations of symmetric generative adversarial networks. A symmetric encoder-decoder generator embedded with acupoint information perception blocks and a multiscale block discriminator capable of processing features in different receptive fields are designed on the basis of the facial image characteristics. Multiple loss functions are used to constrain the acupoint localization network. The results show that the proposed algorithm achieves outcomes similar to those of manual localization, thus offering a novel perspective for the development of intelligent facial acupoint localization technology.
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备注/Memo
收稿日期:2024-10-9。
基金项目:山西省基础研究计划面上项目(202403021221140)；山西省卫生健康委员会中医药创新团队项目(zyytd2024025)；山西中医药大学博士科研启动基金项目(2023BK57).
作者简介:杨婕，副教授，博士，中华中医药学会中医药信息学分会委员、山西医学会医学信息学会委员。主要研究方向为医学图像处理与应用、智能信息处理。获得发明专利授权4项，发表学术论文40余篇。E-mail：yjsxtcm@sxtcm.edu.cn。;高阳，硕士研究生，主要研究方向为医学图像处理与应用、图像识别。E-mail：1220971382@qq.com。;上官宏，副教授，博士，主要研究方向为医学图像处理与应用、模式识别、工业无损检测。主持国家级项目1项，获发明专利授权3项，发表学术论文20余篇。E-mail：shangguan_hong@tyust.edu.cn。
通讯作者:上官宏. E-mail：shangguan_hong@tyust.edu.cn
更新日期/Last Update:
1900-01-01