Document detail
ID
oai:arXiv.org:2403.12719
Topic
Computer Science - Machine Learnin...Author
Aviles-Rivero, Angelica I. Cheng, Chun-Wun Deng, Zhongying Kourtzi, Zoe Schönlieb, Carola-BibianeCategory
Computer Science
Year
2024
listing date
3/27/2024
Keywords
introduce multi-modal alzheimer hypergraphAbstract
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life.
This challenge is tackled through a semi-supervised multi-modal diagnosis framework.
In particular, we introduce a new hypergraph framework that enables higher-order relations between multi-modal data, while utilising minimal labels.
We first introduce a bilevel hypergraph optimisation framework that jointly learns a graph augmentation policy and a semi-supervised classifier.
This dual learning strategy is hypothesised to enhance the robustness and generalisation capabilities of the model by fostering new pathways for information propagation.
Secondly, we introduce a novel strategy for generating pseudo-labels more effectively via a gradient-driven flow.
Our experimental results demonstrate the superior performance of our framework over current techniques in diagnosing Alzheimer's disease.
Aviles-Rivero, Angelica I.,Cheng, Chun-Wun,Deng, Zhongying,Kourtzi, Zoe,Schönlieb, Carola-Bibiane, 2024, Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
A Novel MR Imaging Sequence of 3D-ZOOMit Real Inversion-Recovery Imaging Improves Endolymphatic Hydrops Detection in Patients with Ménière Disease
ménière disease p < detection imaging sequences 3d-zoomit 3d endolymphatic real tse reconstruction ir inversion-recovery hydrops ratio
