Autism spectrum disorders detection based on multi-task transformer neural network

Autism spectrum disorders detection based on multi-task transformer neural network

Blog Article

Abstract Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication.Identifying ASD patients based on resting-state Chair functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism.And it is difficult to effectively identify ASD patients with a single data source (single task).

Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model.Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model.The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity.

This work provides a new perspective and solution for ASD identification based on rs-fMRI MENS TOILETRY BAGS data using multi-task learning.It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.