Multi-Dimension-Embedding-Aware Modality Fusion Transformer for Psychiatric Disorder Clasification

• URL: http://arxiv.org/abs/2310.02690v1
• Date: Wed, 4 Oct 2023 10:02:04 GMT
• Title: Multi-Dimension-Embedding-Aware Modality Fusion Transformer for Psychiatric Disorder Clasification
• Authors: Guoxin Wang, Xuyang Cao, Shan An, Fengmei Fan, Chao Zhang, Jinsong Wang, Feng Yu, Zhiren Wang
• Abstract summary: We construct a deep learning architecture that takes as input 2D time series of rs-fMRI and 3D volumes T1w. We show that our proposed MFFormer performs better than that using a single modality or multi-modality MRI on schizophrenia and bipolar disorder diagnosis.
• Score: 13.529183496842819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning approaches, together with neuroimaging techniques, play an important role in psychiatric disorders classification. Previous studies on psychiatric disorders diagnosis mainly focus on using functional connectivity matrices of resting-state functional magnetic resonance imaging (rs-fMRI) as input, which still needs to fully utilize the rich temporal information of the time series of rs-fMRI data. In this work, we proposed a multi-dimension-embedding-aware modality fusion transformer (MFFormer) for schizophrenia and bipolar disorder classification using rs-fMRI and T1 weighted structural MRI (T1w sMRI). Concretely, to fully utilize the temporal information of rs-fMRI and spatial information of sMRI, we constructed a deep learning architecture that takes as input 2D time series of rs-fMRI and 3D volumes T1w. Furthermore, to promote intra-modality attention and information fusion across different modalities, a fusion transformer module (FTM) is designed through extensive self-attention of hybrid feature maps of multi-modality. In addition, a dimension-up and dimension-down strategy is suggested to properly align feature maps of multi-dimensional from different modalities. Experimental results on our private and public OpenfMRI datasets show that our proposed MFFormer performs better than that using a single modality or multi-modality MRI on schizophrenia and bipolar disorder diagnosis.

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