Search Results for author:
Found 3 papers, 1 papers with code
Random Forest regression for manifold-valued responses
An increasing array of biomedical and computer vision applications requires the predictive modeling of complex data, for example images and shapes.
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data.
Random Forests on Distance Matrices for Imaging Genetics Studies
We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes representing the human brain's structure or function, and obtained using neuroimaging techniques.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
