BrainNPT: Pre-training of Transformer networks for brain network classification

• URL: http://arxiv.org/abs/2305.01666v4
• Date: Wed, 2 Aug 2023 09:37:14 GMT
• Title: BrainNPT: Pre-training of Transformer networks for brain network classification
• Authors: Jinlong Hu, Yangmin Huang, Nan Wang, Shoubin Dong
• Abstract summary: We proposed a Transformer-based neural network, named as BrainNPT, for brain functional network classification. We proposed a pre-training framework for BrainNPT model to leverage unlabeled brain network data. The results of classification experiments demonstrated the BrainNPT model without pre-training achieved the best performance.
• Score: 3.8906116457135966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning methods have advanced quickly in brain imaging analysis over the past few years, but they are usually restricted by the limited labeled data. Pre-trained model on unlabeled data has presented promising improvement in feature learning in many domains, including natural language processing and computer vision. However, this technique is under-explored in brain network analysis. In this paper, we focused on pre-training methods with Transformer networks to leverage existing unlabeled data for brain functional network classification. First, we proposed a Transformer-based neural network, named as BrainNPT, for brain functional network classification. The proposed method leveraged <cls> token as a classification embedding vector for the Transformer model to effectively capture the representation of brain network. Second, we proposed a pre-training framework for BrainNPT model to leverage unlabeled brain network data to learn the structure information of brain networks. The results of classification experiments demonstrated the BrainNPT model without pre-training achieved the best performance with the state-of-the-art models, and the BrainNPT model with pre-training strongly outperformed the state-of-the-art models. The pre-training BrainNPT model improved 8.75% of accuracy compared with the model without pre-training. We further compared the pre-training strategies, analyzed the influence of the parameters of the model, and interpreted the trained model.

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