MHATC: Autism Spectrum Disorder identification utilizing multi-head attention encoder along with temporal consolidation modules

• URL: http://arxiv.org/abs/2201.00404v1
• Date: Mon, 27 Dec 2021 07:50:16 GMT
• Title: MHATC: Autism Spectrum Disorder identification utilizing multi-head attention encoder along with temporal consolidation modules
• Authors: Ranjeet Ranjan Jha, Abhishek Bhardwaj, Devin Garg, Arnav Bhavsar, Aditya Nigam
• Abstract summary: Resting-state fMRI is commonly used for diagnosing Autism Spectrum Disorder (ASD) by using network-based functional connectivity. 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.
• Score: 11.344829880346353
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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