AVILES-RIVERO, Angelica I.; RUNKEL, Christina; PAPADAKIS, Nicolas; KOURTZI, Zoe; SCHÖNLIEB, Carola-Bibiane

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AVILES-RIVERO, Angelica I.
Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]

RUNKEL, Christina
Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP]

PAPADAKIS, Nicolas
Institut de Mathématiques de Bordeaux [IMB]

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Communication dans un congrès

Ce document a été publié dans

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'22), 2022-09-18, Singapour.

Résumé en anglais

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.< Réduire

URI

https://oskar-bordeaux.fr/handle/20.500.12278/191175

Projet Européen

Nonlocal Methods for Arbitrary Data Sources

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Importé de hal

Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251