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.
@article{arxiv.2403.12719,
title = {Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis},
author = {Angelica I. Aviles-Rivero and Chun-Wun Cheng and Zhongying Deng and Zoe Kourtzi and Carola-Bibiane Schönlieb},
journal= {arXiv preprint arXiv:2403.12719},
year = {2024}
}