Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
This work addresses the need for accurate early diagnosis of Alzheimer's disease to enhance patient treatment, representing an incremental advancement in multi-modal classification methods.
The paper tackles the problem of early Alzheimer's disease diagnosis by developing a semi-supervised hypergraph learning framework that incorporates multi-modal imaging and non-imaging data with higher-order relations, achieving improved performance over existing techniques.
The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider either lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer's disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy for constructing a robust hypergraph that preserves the data semantics. We achieve this by enforcing perturbation invariance at the image and graph levels using a contrastive based mechanism. Secondly, we present a dynamically adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the predictive uncertainty. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.