使用拉曼光谱进行无监督域适应,用于快速诊断自身免疫性疾病
Ziyang Zhang1, Yang Liu1, Cheng Chen1
1College of Software, Xinjiang University, Urumqi 830046, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了一种新的框架,用于使用拉曼光谱和人工智能诊断自身免疫性疾病. 该方法在无标签的疾病诊断中实现了高精度,克服了数据限制.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 自身免疫性疾病是常见的,但由于复杂的症状和有限的标记数据,诊断是具有挑战性的.
- 传统的计算机辅助诊断 (CAD) 方法需要广泛的,可靠的注释数据集,这对于自身免疫性疾病来说很少.
研究的目的:
- 开发一种无标签,无监督的转移诊断框架,用于自身免疫性疾病.
- 将拉曼光谱与域适应集成在一起,以提高诊断准确度.
主要方法:
- 提出了一个基于伪标签的条件域对抗网络 (CDAN-PL) 框架.
- 利用拉曼光谱学与域适应技术相结合.
- 集成的光谱数据适应特征提取和伪标签生成,用于强大的对抗性学习.
主要成果:
- 在同类转移任务中实现了92.3%的平均准确性,超过了基线模型 (80.81%,86.4%).
- 在非同类转移任务中表现出强大的概括性,平均准确率为90.05%.
- 验证了CDAN-PL在基于拉曼光谱的疾病诊断中的优越性.
结论:
- 该CDAN-PL框架使有效的无标签,无监督转移诊断自身免疫性疾病.
- 该方法解决了自身免疫性疾病诊断中有限的标记数据的挑战.
- 这种方法显示出在复杂疾病中推进人工智能驱动的诊断工具的巨大潜力.
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