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脑功能网络生成使用分布规则化的对抗图形自编码器与变压器用于痴呆症诊断.

Qiankun Zuo1,2, Junhua Hu3, Yudong Zhang4

  • 1School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China.

Computer modeling in engineering & sciences : CMES
|April 3, 2024
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概括

这项研究引入了一种新的方法来生成合成大脑功能网络,提高痴呆症诊断的准确性. 该方法通过创建多样化,现实的网络数据来改进数据驱动模型,以便更好地分析疾病.

关键词:
敌对图形编码器的编码器.痴呆症 痴呆症是一种痴呆症.功能性大脑连接性 功能性大脑连接性发电变压器是一个发电变压器.图表 卷积网络 卷积网络标签的分发,标签的分发.

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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:

  • 大脑功能网络通过揭示异常连接提供了对痴呆症疾病的洞察力.
  • 有限的大脑功能网络数据阻碍了数据驱动的痴呆症诊断模型.
  • 现有的模型在数据稀缺的情况下扎,以进行可靠的痴呆症分析.

研究的目的:

  • 开发一种用于生成合成大脑功能网络的新方法,以增强数据集.
  • 提高数据驱动的痴呆症诊断模型使用生成数据的准确性.
  • 增强脑网络的表示学习的稳定性和稳定性.

主要方法:

  • 提出了一种分布规范化的对抗图形自动编码器 (DAGAE) 与变压器发生器.
  • 利用标签分布估计来规范潜伏空间以实现稳定的学习.
  • 使用变压器发电机来创建节点到节点连接,保留拓性质.

主要成果:

  • 生成的大脑功能网络改善了不同分类器的预测性能.
  • 在阿尔茨海默病神经成像倡议 (ADNI) 数据集上实现了85.33%的分类准确性.
  • 与其他数据增强模型相比,表现出卓越的性能.

结论:

  • 提出的DAGAE模型有效地产生多样化的大脑功能网络.
  • 用生成网络增强数据显著提高了认知疾病诊断的准确性.
  • 该模型对分析除痴呆症之外的各种认知障碍具有前途.