Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification

TL;DR

This paper proposes a semi-supervised hypergraph diffusion network that leverages multi-modal data and higher-order relations for early Alzheimer's disease diagnosis, improving accuracy with minimal labeled data.

Contribution

It introduces a novel hypergraph learning framework with dual embedding and a dynamic diffusion model for better multi-modal data integration in Alzheimer's diagnosis.

Findings

Outperforms existing methods in Alzheimer's classification accuracy

Effectively utilizes limited labeled data through semi-supervised learning

Enhances predictive uncertainty estimation in diagnosis

Abstract

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.