Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
This addresses Alzheimer's diagnosis for patients and clinicians, with incremental improvements in multi-modal learning.
The paper tackles early detection of Alzheimer's disease using a semi-supervised multi-modal framework with a new hypergraph approach that enables higher-order relations between data types while using minimal labels, demonstrating superior performance over current techniques.
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.