一个新的语言特征提取框架的框架,基于语言下静脉细分的框架.
IEEE transactions on nanobioscience
|September 17, 2024
概括
这项研究引入了一种新方法,用于在传统中医 (TCM) 舌头诊断中分析舌下静脉特征. 该方法通过改善舌下静脉细分和特征提取来提高血液循环评估.
科学领域:
- 综合医学是一个整体医学.
- 医学成像分析 医学成像分析
- 计算生物学 计算生物学
背景情况:
- 舌下静脉的特征 (胀,静脉缩,蓝斑症) 是传统中医 (TCM) 舌头诊断的关键指标,用于评估血液循环.
- 由于有限的数据集和图像噪声,对这些特征进行自动和准确的提取是具有挑战性的.
研究的目的:
- 开发一种创新的方法来提取舌下静脉特征,以改善TCM舌头诊断.
- 解决现有方法的局限性,重点关注舌下静脉细分,而不是整个舌头底部.
主要方法:
- 开发了一个使用Polyp-PVT网络来减少噪音的语言下静脉细分框架.
- 采用基于变压器的方法 (Swin-Transformer) 来进行舌下静脉特征提取.
- 创建了一个全面的亚语言静脉图像数据集,以支持细分和分类.
主要成果:
- 拟议的方法有效地细分下舌静脉,消除周围区域的噪音.
- 基于变压器的特征提取显示了显著的改进.
- 实验结果显示,与现有的舌头特征提取技术相比,其性能优越.
结论:
- 这种新的舌头特征提取方法,包括舌下静脉细分,在TCM诊断方面取得了重大进展.
- 这种方法提高了通过舌下静脉分析评估血液循环的准确性和效率.
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