智能系统学报2025，Vol.20Issue(2)：400-406,7.DOI:10.11992/tis.202402025
基于Transformer模型的自闭症功能磁共振图像分类
Classification of functional magnetic resonance images for autism based on Transformer model
摘要
Abstract
Current classification models of functional magnetic resonance(fMRI)images for autism struggle with low classification accuracy across datasets from multiple institutions.Thus,they have difficulty assisting in the diagnosis of autism.This study proposes a Transformer-based autism classification model named TransASD to address this issue.This model utilizes brain mapping templates to extract time series from fMRI data and incorporates an overlapping win-dow attention mechanism to better capture local and global features of heterogeneous data.A cross-window regulariza-tion method is also proposed as an additional loss term,which allows the model to focus more accurately on important features.In this study,we use the model to conduct experiments on the publicly available autism dataset ABIDE,under the ten-fold cross-validation method,the accuracy rate is 71.44%.Experimental results show that the model achieves state-of-the-art performance compared with other advanced algorithmic models.关键词
深度学习/Transformer/注意力机制/自闭症/功能磁共振成像/图像分类/特征提取/功能连接Key words
deep learning/Transformer/attention mechanism/autism/functional magnetic resonance imaging/image classification/feature extraction/functional connectivity分类
信息技术与安全科学引用本文复制引用
潘登,毕晓君..基于Transformer模型的自闭症功能磁共振图像分类[J].智能系统学报,2025,20(2):400-406,7.基金项目
国家自然科学基金重点项目(62236011) （62236011）
国家社科基金重大项目(20&ZD279). （20&ZD279）
