Alzheimer's Disease (AD) is a prevalent and severe neurodegenerative disorder, and early diagnosis is essential for managing disease progression. Recently, multimodal graph learning has demonstrated significant potential in integrating both medical imaging and non-imaging data, as well as uncovering relationships between patients. However, the high-dimensional nature of multimodal medical data poses significant challenges for constructing and learning modality graph structures. Moreover, existing methods are often imprecise in modeling graph structures for continuous data. To address these issues, this paper introduces a novel multimodal multi-graph fusion learning method for Alzheimer's disease diagnosis. Specifically, multimodal state space networks (multimodal SSNs) are proposed to capture the dependencies between multimodal and high-dimensional features. Furthermore, a novel graph structure learning (KGSL) based on an initial K-nearest neighbors graph is proposed to separately construct graph structures for each modality. This method is particularly suitable for modeling the graph structures of Euclidean data. Finally, multimodal graph fusion integrates various modal graph structures into a single graph, leading to enhanced multimodal integration. In addition, this paper uses a learnable Chebyshev Graph Convolutional Network for the classification network, which enables end-to-end optimization. Experimental results demonstrate that our approach achieves excellent performance on public datasets.

Multimodal image fusion integrates information from different modalities to generate images with complementary features. However, existing medical image fusion methods often lack interactive guidance tailored to specific subjective and objective needs, resulting in a uniform processing approach for all image regions within a single model. This limitation does not fully account for region-specific characteristics, restricting the ability of the model to effectively address degradation issues encountered during image acquisition.To overcome these challenges, we propose a novel semantic text-guided medical image fusion and interactive degradation handling method called STGFuse. On one hand, the text-guided semantic encoder enables integrated processing of both image degradation and fusion tasks. It allows for text-based selection of regions and effects, focusing on preserving critical features from different images and producing visually compelling fusion results. On the other hand, we introduce a dedicated interactive fusion module, enhancing the synergistic interaction between visual and textual information and facilitating more profound cross-modal integration. Extensive experiments demonstrate the effectiveness of STGFuse, with both qualitative and quantitative evaluations proving its superiority over existing methods in addressing fusion challenges caused by image degradation.