Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
| hal.structure.identifier | Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP] | |
| dc.contributor.author | AVILES-RIVERO, Angelica I. | |
| hal.structure.identifier | Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP] | |
| dc.contributor.author | RUNKEL, Christina | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | PAPADAKIS, Nicolas | |
| hal.structure.identifier | University of Cambridge [UK] [CAM] | |
| dc.contributor.author | KOURTZI, Zoe | |
| hal.structure.identifier | Department of Applied Mathematics and Theoretical Physics [Cambridge] [DAMTP] | |
| dc.contributor.author | SCHÖNLIEB, Carola-Bibiane | |
| dc.date.accessioned | 2024-04-04T02:41:43Z | |
| dc.date.available | 2024-04-04T02:41:43Z | |
| dc.date.conference | 2022-09-18 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191175 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | en | |
| dc.title.en | Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification | |
| dc.type | Communication dans un congrès | |
| dc.subject.hal | Informatique [cs]/Imagerie médicale | |
| dc.identifier.arxiv | 2204.02399 | |
| dc.description.sponsorshipEurope | Nonlocal Methods for Arbitrary Data Sources | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'22) | |
| bordeaux.country | SG | |
| bordeaux.conference.city | Singapour | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-03634109 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.conference.end | 2022-06-22 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03634109v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=AVILES-RIVERO,%20Angelica%20I.&RUNKEL,%20Christina&PAPADAKIS,%20Nicolas&KOURTZI,%20Zoe&SCH%C3%96NLIEB,%20Carola-Bibiane&rft.genre=unknown |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
|
There are no files associated with this item. |
|||