Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis

arXiv:2403.12719v1 Announce Type: new
Abstract: 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 …

abstract alzheimer's arxiv challenge cs.lg data detection diagnosis disease framework hypergraph labels life modal multi-modal networks patient quality relations semi-supervised through type