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通过双视图连接的动态图形变压器网络,用于自闭症谱系障碍识别

Zihao Guan¹, Jiaming Yu¹, Zhenshan Shi²

¹College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Computers in biology and medicine

|April 10, 2024

概括

这项研究引入了一个新的动态图形变压器网络用于自闭症谱系障碍 (ASD) 识别. 该方法有效地使用正和负的功能连接,以提高ASD诊断的准确性.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

自闭症谱系障碍 (ASD) 诊断需要客观的早期干预方法.
现有的功能连接 (FC) 分析方法忽略了正负FC中的补充信息,并需要预定义的图表.
在ASD识别方面的挑战包括类不平衡和多站点数据异质性.

研究的目的:

为准确的自闭症谱系障碍 (ASD) 识别提出一个新的动态图形变压器网络.
为了利用双视图连接 (正负FC) 进行增强的信息提取.
解决阶级不平衡问题,提高多站点数据集中的模型通用性.

主要方法:

开发了一种双视图特征提取器,以捕获来自正负连接的个别和互补信息.
创新了一个带有动态KNN模块的图形转换器网络,用于在没有额外信息的情况下构建图形.
实施了PolyLoss和Vrex方法,以减轻类不平衡并提高通用性.

主要成果:

与最先进的技术相比,拟议的方法在ASD识别方面取得了更高的性能.
在1102名受试者的ABIDE I数据集上证明了令人满意的概括性.
有效地利用正负两种功能连接来提高诊断准确度.

结论:

新的动态图形变压器网络为客观和准确的ASD识别提供了一个有希望的方法.
双视图连接分析显著提高了模型区分ASD和对照对象的能力.
该方法显示了在现实世界的临床环境中强大的应用潜力,解决了以前方法的关键局限性.

关键词:

自闭症谱系障碍自闭症谱系障碍域名通用化域名通用化双视图连接双视图连接动态的人口图形.图形变压器图形变压器

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