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

• URL: http://arxiv.org/abs/2204.02399v1
• Date: Mon, 4 Apr 2022 10:31:42 GMT
• Title: Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
• Authors: Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis, Zoe Kourtzi, Carola-Bibiane Sch\"onlieb
• Abstract summary: 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. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
• Score: 4.179845212740817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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