Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis

• URL: http://arxiv.org/abs/2403.12719v1
• Date: Tue, 19 Mar 2024 13:28:03 GMT
• Title: Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
• Authors: Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng, Zoe Kourtzi, Carola-Bibiane Schönlieb,
• Abstract summary: Early detection of Alzheimer's disease's precursor stages is imperative for enhancing patient outcomes and quality of life. We introduce a new hypergraph framework that enables higher-order relations between multi-modal data, while utilising minimal labels. Our experimental results demonstrate the superior performance of our framework over current techniques in diagnosing Alzheimer's disease.
• Score: 12.42019222822497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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