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.

翻译：阿尔茨海默病前驱阶段的早期检测对于显著改善患者预后及生活质量至关重要。本研究通过半监督多模态诊断框架应对这一挑战。具体而言，我们提出了一种新型超图框架，该框架能够在利用最少标签的同时实现多模态数据间的高阶关系建模。首先，我们引入双层级超图优化框架，该框架联合学习图增强策略与半监督分类器。这种双重学习策略通过促进信息传播的新路径，有望增强模型的鲁棒性与泛化能力。其次，我们提出一种创新的伪标签生成策略，通过梯度驱动流更高效地生成伪标签。实验结果表明，本框架在阿尔茨海默病诊断任务中显著优于现有技术。