论文标题
MHATC：使用多头注意编码器以及时间整合模块的自闭症谱系障碍识别
MHATC: Autism Spectrum Disorder identification utilizing multi-head attention encoder along with temporal consolidation modules
论文作者
论文摘要
静止状态fMRI通常用于使用基于网络的功能连接性来诊断自闭症谱系障碍（ASD）。已经表明，ASD与大脑区域及其相互联系有关。但是，根据对照人群的成像数据和ASD患者大脑的成像数据之间的连通性模式进行区分是一项非平凡的任务。为了解决上述分类任务，我们提出了一种新颖的深度学习体系结构（MHATC），该结构包括多头关注和时间固结模块，用于将个人分类为ASD的患者。设计的体系结构是对当前深层神经网络解决方案的局限性的深入分析。我们的方法不仅强大，而且在计算上是有效的，这可以在其他各种研究和临床环境中采用。
Resting-state fMRI is commonly used for diagnosing Autism Spectrum Disorder (ASD) by using network-based functional connectivity. It has been shown that ASD is associated with brain regions and their inter-connections. However, discriminating based on connectivity patterns among imaging data of the control population and that of ASD patients' brains is a non-trivial task. In order to tackle said classification task, we propose a novel deep learning architecture (MHATC) consisting of multi-head attention and temporal consolidation modules for classifying an individual as a patient of ASD. The devised architecture results from an in-depth analysis of the limitations of current deep neural network solutions for similar applications. Our approach is not only robust but computationally efficient, which can allow its adoption in a variety of other research and clinical settings.
