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相关实验视频

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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基于双流视觉变压器的多标签识别,用于TCM处方的建设.

Zijuan Zhao1, Yan Qiang2, Fenghao Yang1

  • 1College of Computer Science and Technology(College of Data Science), Taiyuan University of Technology, Taiyuan, 030002, Shanxi, China.

Computers in biology and medicine
|January 20, 2024
PubMed
概括

本研究介绍了使用视觉诊断图像进行传统中医 (TCM) 草药处方构建的自动化框架. 该模型实现了显著的精度和回忆,证明了整合视觉数据用于TCM建议的可行性.

关键词:
面部和舌头的图像.图表 卷积网络 卷积网络多标签图像识别多标签图像识别处方 建筑施工 要求视觉变压器 视觉变压器

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科学领域:

  • 综合医学是一个整体的医学.
  • 医疗保健中的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 传统中医 (TCM) 依赖于视觉诊断 (面部,舌头图像) 进行治疗.
  • 从视觉数据中自动化草药处方构建对于移动医疗保健和理解特征-草药相关性非常有价值.

研究的目的:

  • 提出基于视觉诊断图像的自动化多草药推的新框架.
  • 探索多视角视觉数据的整合,以实现准确的TCM处方生成.

主要方法:

  • 一个使用视觉变压器和多标签分类的多草药推框架.
  • 关键组件包括用于图像编码的双流视觉变压器,用于标签嵌入的图形卷积网络,以及用于跨模态融合的多模态因子双线模块.
  • 开发了一个端到端的多标签图像草药推模型.

主要成果:

  • 该框架在真实面部和舌头图像上获得了50.06%的精度,48.33%的回忆率和49.18%的F1得分.
  • 生成的处方数据与现实世界样本非常相似.

结论:

  • 该研究验证了使用视觉诊断信息自动化草药处方构建的可行性.
  • 为开发利用物理和视觉数据的自动化TCM处方系统提供了宝贵的见解.