Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
AVILES-RIVERO, Angelica I.
Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]
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Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]
AVILES-RIVERO, Angelica I.
Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]
Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]
SCHÖNLIEB, Carola-Bibiane
Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]
< Reduce
Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]
Language
en
Communication dans un congrès
This item was published in
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'22), 2022-09-18, Singapour.
English 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 ...Read more >
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.Read less <
European Project
Nonlocal Methods for Arbitrary Data Sources
Origin
Hal imported