基于GIN转换器的双向图对比学习框架
Shufeng Zhou1, Lina Zhou1, Yueying Zhou1
1School of Mathematics Science, Liaocheng University, Liaocheng Shandong, 252000, China.
概括
这项研究介绍了GITrans-PairCL,一种无监督的深度学习方法,使用静态fMRI数据来改善自闭症谱系障碍和严重抑郁症的诊断. 新的框架通过从有限的标记数据中学习来提高准确性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 休息状态功能磁共振成像 (rs-fMRI) 对于诊断自闭症谱系障碍 (ASD) 和主要抑郁障碍 (MDD) 等神经精神疾病至关重要.
- 目前用于rs-fMRI分析的深度学习模型需要大量的标记数据,这阻碍了其临床应用.
研究的目的:
- 开发一个无监督的深度学习框架,GITrans-PairCL,以克服对神经精神疾病的rs-fMRI分析中的数据短缺.
- 集成图形同态网络 (GIN) 和变压器架构,从rs-fMRI数据中进行多尺度的特征提取.
主要方法:
- 提出了一种基于GIN转换器的对向图对比学习框架 (GITrans-PairCL),其中包括双模对比学习 (DCL) 和任务驱动微调 (TDF) 模块.
- DCL使用滑窗增强的rs-fMRI时间序列,使用GIN进行本地空间连接,并使用变压器进行全球时间动态.
- 交叉视图对比学习用于多尺度特征提取,然后对下游分类任务进行微调.
主要成果:
- 与传统的机器学习和深度学习基线相比,GITrans-PairCL在自动脑疾病诊断中表现优越.
- 该模型在对公共数据集的单站点和跨站点评估中实现了高准确性.
- 该框架有效地结合了本地和全球特征,减少了对标记数据的依赖.
结论:
- GITrans-PairCL框架提供了一种有希望的无监督方法,用于使用rs-fMRI数据诊断大脑疾病.
- 这种方法增强了模型的概括性,减少了在临床环境中需要广泛的标记数据集的需求.
- 整合GIN和变压器架构使得有效的多尺度特征学习能够提高诊断准确性.
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