22 citations · 27 across the 5 of their papers we have counts for
5 papers
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
Tim Hahn, Jan Ernsting, Nils R. Winter +31
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a corn…
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
Sebastian G. Popescu, David J. Sharp, James H. Cole +2
We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably…
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
Jessica Dafflon, Walter H. L Pinaya, Federico Turkheimer +7
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to…
Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model
Dajiang Zhu, Brandalyn C. Riedel, Neda Jahanshad +13
Large-scale collaborative analysis of brain imaging data, in psychiatry and neu-rology, offers a new source of statistical power to discover features that boost ac-curacy in diseas…
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
James H Cole, Rudra PK Poudel, Dimosthenis Tsagkrasoulis +4
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people and deviations from healthy brain ageing have been associated with cogniti…