MHATC: Autism Spectrum Disorder identification utilizing multi-head attention encoder along with temporal consolidation modules
This work addresses the challenge of accurately diagnosing ASD from brain connectivity patterns, which could aid clinical settings, though it appears incremental in improving existing deep learning methods.
The authors tackled the problem of classifying Autism Spectrum Disorder (ASD) from resting-state fMRI data by proposing MHATC, a deep learning architecture with multi-head attention and temporal consolidation modules, which achieved robust and computationally efficient results.
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.