4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification
This work addresses the challenge of improving ASD diagnosis accuracy for medical applications, representing an incremental advance over existing methods.
The paper tackled the problem of classifying Autism Spectrum Disorder (ASD) from fMRI data by proposing a 4D spatio-temporal deep learning approach that jointly learns from spatial and temporal information, achieving an F1-score of 0.71 compared to 0.65 from a previous method.
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.