Modeling longitudinal imaging biomarkers with parametric Bayesian multi‐task learning

Author:

Aksman Leon M.1ORCID,Scelsi Marzia A.1,Marquand Andre F.2,Alexander Daniel C.1,Ourselin Sebastien13,Altmann Andre1,

Affiliation:

1. Centre for Medical Image Computing University College London London UK

2. Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen The Netherlands

3. School of Biomedical Engineering and Imaging Sciences St Thomas' Hospital, King's College London London UK

Funder

Engineering and Physical Sciences Research Council

FP7 Information and Communication Technologies

Horizon 2020 Framework Programme

Medical Research Council Canada

National Institute for Health Research

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

Wiley

Subject

Clinical Neurology,Neurology,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

Reference61 articles.

1. Abadi M. Barham P. Chen J. Chen Z. Davis A. Dean J. Devin M. Ghemawat S. Irving G. Isard M. Kudlur M. Levenberg J. Monga R. Moore S. Murray D.G. Steiner B. Tucker P. Vasudevan V. Warden P. Wicke M. Yu Y. Zheng X. 2016.TensorFlow: A system for large‐scale machine learning.

2. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

3. Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

4. Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data

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