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Found 6 papers, 1 papers with code
Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction
In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i. e., trajectories) and stratifies individuals without using clinical diagnosis for model training.
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.
HGIB: Prognosis for Alzheimer's Disease via Hypergraph Information Bottleneck
We demonstrate, through extensive experiments on ADNI, that our proposed HGIB framework outperforms existing state-of-the-art hypergraph neural networks for Alzheimer's disease prognosis.
Safe AI for health and beyond -- Monitoring to transform a health service
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets.
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
CAFLOW: Conditional Autoregressive Flows
We model the conditional distribution of the latent encodings by modeling the auto-regressive distributions with an efficient multi-scale normalizing flow, where each conditioning factor affects image synthesis at its respective resolution scale.
