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 architecture with two pre-training strategies 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 fine-tuned
model.

本文提出了一种基于 Transformer 的深度学习网络，名为 BrainNPT，用于脑功能网络分类，并探讨了使用未标记数据进行预训练的方法，结果表明使用预训练的 BrainNPT 模型可以显著提高分类表现。