Year: 2025
Type: article
Abstract: Alzheimer's disease progression prediction is critical for patients with early Mild Cognitive Impairment (MCI) to enable timely intervention and improve their quality of life. While existing progression prediction techniques demonstrate potential with multimodal data, they are highly limited by their reliance on labelled data and fail to account for a key element of future progression prediction: not all features extracted at the current moment may be relevant for predicting progression several years later. To address these limitations in the literature, we design a novel semi-supervised multimodal learning hypergraph architecture, termed HIBMatch, by harnessing hypergraph knowledge based on information bottleneck and consistency regularisation strategies. Firstly, our framework utilises hypergraphs to represent multimodal data, encompassing both imaging and non-imaging modalities. Secondly, to harmonise relevant information from the currently captured data for future MCI conversion prediction, we propose a Hypergraph Information Bottleneck (HIB) that discriminates against irrelevant information, thereby focusing exclusively on harmonising relevant information for future MCI conversion prediction. Thirdly, our method enforces consistency regularisation between the HIB and a discriminative classifier to enhance the robustness and generalisation capabilities of HIBMatch under both topological and feature perturbations. Finally, to fully exploit the unlabeled data, HIBMatch incorporates a cross-modal contrastive loss for data efficiency. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed HIBMatch framework surpasses existing state-of-the-art methods in Alzheimer's disease prognosis.
Source: Apollo (University of Cambridge)
DOI: 10.17863/cam.123205
Authors: Deng, Zhongying, Wang, Shujun, Aviles-Rivero, Angelica I, Kourtzi, Zoe, Schonlieb, Carola-Bibiane
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: W. F. Albright Institute of Archaeological Research, Alzheimer's Disease Neuroimaging Initiative, Wellcome Trust, Leverhulme Trust, European Commission, Hong Kong Polytechnic University, Directorate for Biological Sciences, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council
Awards: All in one cancer imaging optimisation using an integrated mathematical and deep learning approach, Nonlocal Methods for Arbitrary Data Sources, Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India, Flexible perception: functional plasticity mechanisms in the human brain, G434337789, Adaptive decision templates in the human brain, Cambridge Mathematics of Information in Healthcare (CMIH), EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, Combining Knowledge And Data Driven Approaches to Inverse Imaging Problems, PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation, Predicting Dementia: Optimising and translating AI to improve prognosis and clinical pathways
OpenAlex ID: W7106157370
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