[1]PAN Deng,BI Xiaojun.Classification of functional magnetic resonance images for autism based on Transformer model[J].CAAI Transactions on Intelligent Systems,2025,20(2):400-406.[doi:10.11992/tis.202402025]
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Classification of functional magnetic resonance images for autism based on Transformer model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 400-406 Column: 学术论文—机器感知与模式识别 Public date: 2025-03-05
Title:
Classification of functional magnetic resonance images for autism based on Transformer model
Author(s):
PAN Deng1;  BI Xiaojun2; 3
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Beijing 100081, China;
3. Department of Informati
Keywords:
deep learning; Transformer; attention mechanism; autism; functional magnetic resonance imaging; image classification; feature extraction; functional connectivity
CLC:
TP391
DOI:
10.11992/tis.202402025
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 window attention mechanism to better capture local and global features of heterogeneous data. A cross-window regularization 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.
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