论文标题
4D时空深度学习与4D fMRI数据用于自闭症谱系障碍分类
4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification
论文作者
论文摘要
自闭症谱系障碍（ASD）与行为和沟通问题有关。通常，功能磁共振成像（fMRI）用于检测和表征与该疾病有关的大脑变化。最近，已经采用了机器学习方法来揭示新模式，试图从时空fMRI图像中对ASD进行分类。通常，这些方法侧重于时间或空间信息处理。取而代之的是，我们为ASD分类提出了一种4D时空的深度学习方法，我们可以从空间和时间数据中共同学习。我们采用4D卷积神经网络和卷积转变模型，与F1得分为0.65相比，F1得分为0.71的方法优于先前的方法。
Autism spectrum disorder (ASD) is associated with behavioral and communication problems. Often, functional magnetic resonance imaging (fMRI) is used to detect and characterize brain changes related to the disorder. Recently, machine learning methods have been employed to reveal new patterns by trying to classify ASD from spatio-temporal fMRI images. Typically, these methods have either focused on temporal or spatial information processing. Instead, we propose a 4D spatio-temporal deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D convolutional neural networks and convolutional-recurrent models which outperform a previous approach with an F1-score of 0.71 compared to an F1-score of 0.65.
