Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916166411223040 |
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| author | Aviles-Rivero, Angelica I. Cheng, Chun-Wun Deng, Zhongying Kourtzi, Zoe Schönlieb, Carola-Bibiane |
| author_facet | Aviles-Rivero, Angelica I. Cheng, Chun-Wun Deng, Zhongying Kourtzi, Zoe Schönlieb, Carola-Bibiane |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12719 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis Aviles-Rivero, Angelica I. Cheng, Chun-Wun Deng, Zhongying Kourtzi, Zoe Schönlieb, Carola-Bibiane Machine Learning 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. |
| title | Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.12719 |