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Found 10 papers, 3 papers with code
Self-organized clustering, prediction, and superposition of long-term cognitive decline from short-term individual cognitive test scores in Alzheimer's disease
Various predictive models have been designed to realize its early onset and study the long-term trajectories of cognitive test scores across populations of interest.
A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials
A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect.
Bayesian inference and role of astrocytes in amyloid-beta dynamics with modelling of Alzheimer's disease using clinical data
Therefore, in the present study, we developed and evaluated novel stochastic models for Abeta growth using ADNI data to predict the effect of astrocytes on AD progression in a clinical trial.
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models
Transfer learning has remarkably improved computer vision.
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis
Combining MRI and genetic features improved survival prediction over using either modality alone, but adding CDC to any combination of features only worked as well as using only CDC features.
MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation
First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks.
Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease
Using a pre-defined 0. 5 threshold on DAT scores, we predicted whether or not a subject would develop DAT in the future.
Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network
In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD.
Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease.
High-dimensional regression over disease subgroups
We consider high-dimensional regression over subgroups of observations.
