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Communication Dans Un Congrès Année : 2022

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

Résumé

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.

Dates et versions

hal-03634109 , version 1 (07-04-2022)

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Citer

Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis, Zoe Kourtzi, Carola-Bibiane Schönlieb. Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'22), Sep 2022, Singapour, Singapore. ⟨hal-03634109⟩

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