Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
<|reference_start|>Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis: 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.<|reference_end|>
@article{aviles-rivero2024bilevel,
    title={Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis},
    author={Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng, Zoe
  Kourtzi, Carola-Bibiane Sch"onlieb},
    journal={arXiv preprint arXiv:2403.12719},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12719},
    primaryClass={cs.LG}
}