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arXiv · 2403.12719

Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis

Angelica I. Aviles-Rivero·Chun-Wun Cheng·Zhongying Deng·Zoe Kourtzi·Carola-Bibiane Schönlieb
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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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Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng, Zoe Kourtzi, Carola-Bibiane Schönlieb. 2024-03-19. Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis. https://arxiv.org/abs/2403.12719

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