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一种多视图卷积神经网络方法,结合了注意力机制,用于诊断自闭症谱系障碍

Mingzhi Wang¹, Zhiqiang Ma¹, Yongjie Wang¹

¹College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.

|December 8, 2023

概括

这项研究介绍了MAACNN,一种使用多视图卷积神经网络和注意力机制的新方法,以从fMRI数据中改善自闭症谱系障碍 (ASD) 诊断,实现更高的准确性.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
医疗成像医学成像

背景情况:

自闭症谱系障碍 (ASD) 诊断依赖于主观行为评估.
目前对ASD的诊断模型缺乏足够的准确性.
功能磁共振成像 (fMRI) 提供客观的大脑活动测量,但需要改进的分析模型.

研究的目的:

开发一种准确和客观的方法来识别自闭症谱系障碍 (ASD),使用多尺度fMRI数据.
提出MAACNN,一种混合深度学习模型,将无监督和监督的学习与注意力机制相结合.

主要方法:

使用堆叠的自动编码器来从多尺度fMRI数据中进行无监督的特征提取.
使用多视图卷积神经网络 (CNN) 进行监督分类.
实施了注意力融合机制,用于多尺度数据集成.

主要成果:

在ABIDE-I数据集中,MAACNN获得了75.12%的准确性和0.79 AUC.
该模型在ABIDE-II数据集上显示了72.88%的准确性和0.76 AUC.
提出的方法显著优于现有的ASD诊断模型.

结论:

MAACNN 提供了使用 fMRI 进行 ASD 诊断的强大而准确的方法.
无监督和监督学习与注意力机制的整合提高了诊断性能.
这种方法代表了ASD客观临床诊断的重大进展.

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