穆尔达:多源无监督拉曼光谱域适应模型与重建的目标域用于医疗诊断援助
Yang Liu1, Chen Chen1,2, Enguang Zuo3
1College of Software, Xinjiang University, Urumqi 830046, China.
Analytical chemistry
|September 20, 2024
概括
多源无监督拉曼光谱域适应 (MURDA) 通过从多个光谱数据源转移知识来改善自身免疫疾病的诊断. 这种人工智能驱动的方法在没有大量标记数据的情况下提高了准确性,有助于医学诊断.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 频谱学是一种光谱学.
背景情况:
- 人工智能 (AI) 和拉曼光谱显示出疾病诊断的前景,但需要大量注释的光谱数据.
- 标记医疗数据是耗时和资源密集的,阻碍了准确的诊断模型的开发.
- 现有的方法通常依赖于单一来源的知识传输,限制了概括能力.
研究的目的:
- 开发一种新的方法,即多源无监督拉曼光谱域适应 (MURDA),以减少对标记医疗数据的依赖,以进行人工智能驱动的疾病诊断.
- 通过利用来自多个来源领域的知识来提高自身免疫性疾病的诊断准确性.
- 推出一个专门的特征提取器,双分支多尺度卷积自我注意 (DMCS),优化用于光谱数据.
主要方法:
- 提出了无监督域适应的MURDA模型,将多个来源疾病数据集的知识转移到未标记的目标数据集.
- 开发了DMCS特征提取器,以更好地从光谱数据中提取相关特征.
- 将MURDA和DMCS应用于血清拉曼光谱数据集以诊断自身免疫性疾病,并通过RRUFF数据集进行验证.
主要成果:
- 与传统的单源和多源模型相比,MURDA在三个自身免疫疾病数据集 (73.6%,83.4%,82.9%) 上实现了更高的诊断准确性.
- 与没有域调整的模型相比,观察到显著的改善 (15.1%,36%,21.6%的增加).
- 该方法在不同的拉曼光谱场景中证明了有效性和通用性,包括公开的RRUFF数据集.
结论:
- 拉曼光谱与MURDA模型相结合,有效诊断自身免疫性疾病,显著优于现有方法.
- 该研究强调了多源知识转移的优势,以提高AI诊断模型的稳定性和准确性.
- 研究了关键的光谱峰值,为未来的AI和拉曼光谱研究在疾病诊断中提供了见解.
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