MHATC: Autism Spectrum Disorder identification utilizing multi-head attention encoder along with temporal consolidation modules
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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2021
- DOI:
- 10.48550/arXiv.2201.00404
- arXiv:
- arXiv:2201.00404
- Bibcode:
- 2022arXiv220100404R
- Keywords:
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- Quantitative Biology - Neurons and Cognition;
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning;
- Electrical Engineering and Systems Science - Image and Video Processing