Abstract：
Bipolar disorder (BD) is characterized by recurrent episodes of depression and mild mania. In this paper, to address the common issue of insufficient accuracy in existing methods and meet the requirements of clinical diagnosis, we propose a framework called Spatio-temporal Feature Fusion Transformer (STF2Former). It improves on our previous work - MFFormer by introducing a Spatio-temporal Feature Aggregation Module (STFAM) to learn the temporal and spatial features of rs-fMRI data. It promotes intra-modality attention and information fusion across different modalities. Specifically, this method decouples the temporal and spatial dimensions and designs two feature extraction modules for extracting temporal and spatial information separately. Extensive experiments demonstrate the effectiveness of our proposed STFAM in extracting features from rs-fMRI, and prove that our STF2Former can significantly outperform MFFormer and achieve much better results among other state-of-the-art methods.
摘要：
双相情感障碍（BD）的特征是反复发作的抑郁症和轻度躁狂症。在本文中,为了解决现有方法准确性不足的常见问题，满足临床诊断的要求，我们提出了一个称为时空特征融合变换器(STF2Former)的框架。通过引入时空特征聚合模块（STFAM）来学习rs-fMRI数据的时间和空间特征，它改进了我们以前的工作-MFFormer。它促进了跨不同模态的模态内注意力和信息融合。具体来说,该方法将时间维度和空间维度解耦,设计了两个特征提取模块,分别提取时间信息和空间信息。大量的实验证明了我们提出的STFAM在从rs-fMRI中提取特征的有效性，并证明我们的STF2Former可以显著优于MFFormer，并在其他最先进的方法中取得更好的结果。
