Search Results for author:
Found 6 papers, 0 papers with code
HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression
Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment.
Analysing heterogeneity in Alzheimer Disease using multimodal normative modelling on ATN biomarkers
ADS individuals with moderate or severe dementia showed higher proportion of regional outliers for each modality as well as more dissimilarity in modality-specific regional outlier patterns compared to ADS individuals with early or mild dementia.
Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders
Normative models in neuroimaging learn the brain patterns of healthy population distribution and estimate how disease subjects like Alzheimer's Disease (AD) deviate from the norm.
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases.
Identifying Dementia Subtypes with Electronic Health Records
Our results indicate both inter-subtype variability, which indicates the variability amongst dementia subtypes for a particular component score even with the same CDR and (ii) intra-subtype variability, which indicates the variation in the 6 component scores within a particular dementia subtype.
Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease
However, existing deep learning based normative models on multimodal MRI data use unimodal autoencoders with a single encoder and decoder that may fail to capture the relationship between brain measurements extracted from different MRI modalities.
