Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
Angelica I. Aviles-Rivero Chun-Wun Cheng Zhongying Deng Zoe Kourtzi Carola-Bibiane Schönlieb
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.