PHGNN: A Novel Prompted Hypergraph Neural Network to Diagnose Alzheimer's Disease
This work addresses the critical need for early intervention in Alzheimer's disease by improving diagnostic accuracy, though it appears incremental as it builds on existing hypergraph and prompt learning techniques.
The paper tackles the problem of accurately diagnosing Alzheimer's disease and predicting mild cognitive impairment conversion by addressing challenges like data heterogeneity and limited multimodal data, resulting in a model that outperforms state-of-the-art methods on the ADNI dataset.
The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. However, existing multimodal methods face several challenges, from the heterogeneity of input data, to underexplored modality interactions, missing data due to patient dropouts, and limited data caused by the time-consuming and costly data collection process. In this paper, we propose a novel Prompted Hypergraph Neural Network (PHGNN) framework that addresses these limitations by integrating hypergraph based learning with prompt learning. Hypergraphs capture higher-order relationships between different modalities, while our prompt learning approach for hypergraphs, adapted from NLP, enables efficient training with limited data. Our model is validated through extensive experiments on the ADNI dataset, outperforming SOTA methods in both AD diagnosis and the prediction of MCI conversion.