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相关概念视频

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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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.

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概括
此摘要是机器生成的。

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.

关键词:
大脑疾病诊断 诊断 大脑疾病诊断跨站点的交叉站点.吉恩吉恩是什么意思 吉恩图表对比学习学习的图表.休息状态的fMRI.变压器变压器变压器

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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:

  • 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.