Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

• URL: http://arxiv.org/abs/2303.03388v2
• Date: Sun, 9 Apr 2023 05:29:50 GMT
• Title: Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
• Authors: Junbin Mao, Jin Liu, Hanhe Lin, Hulin Kuang, Shirui Pan and Yi Pan
• Abstract summary: We propose a novel method to offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.
• Score: 29.790200009136825
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning). For the problem of negative impact between modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

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