  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.
