A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer’s Disease Diagnosis

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, where early diagnosis is crucial for improving prognosis and delaying the progression of the disease. Leveraging multimodal PET images, which can reflect various biomarkers like A \(\beta \) and tau protein, is a promising method for AD diagnosis. However, due to the high cost and practical issues of PET imaging, it often faces challenges with incomplete multimodal data. To address this dilemma, in this paper, we propose a Graph-embedded latent Space Learning and Clustering framework, named Graph-SLC, for multiclass AD diagnosis under incomplete multimodal data scenarios. The key concept is leveraging all available subjects, including those with incomplete modality data, to train a network for projecting subjects into their latent representations. These latent representations not only exploit the complementarity of different modalities but also showcase separability among different classes. Specifically, our Graph-SLC consists of three modules, i.e., a multimodal reconstruction module, a subject-similarity graph embedding module, and an AD-oriented latent clustering module. Among them, the multimodal reconstruction module generates subject-specific latent representations that can comprehensively incorporate information from different modalities with guidance from all available modalities. The subject-similarity graph embedding module then enhances the discriminability of different latent representations by ensuring the neighborhood relationships between subjects are preserved in subject-specific latent representations. The AD-oriented latent clustering module facilitates the separability of multiple classes by constraining subject-specific latent representations within the same class to be in the same cluster. Experiments on the ADNI show that our method achieves state-of-the-art performance in multiclass AD diagnosis. Our code is available at https://github.com/Ouzaixin/Graph-SLC .