Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction

Liane S. Canas, Benjamin Yvernault, David M. Cash, Erika Molteni, Tom Veale, Tammie Benzinger, Sébastien Ourselin, Simon Mead, Marc Modat

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Gaussian Processes (GP) are a powerful tool to capture the complex time-variations of a dataset. In the context of medical imaging analysis, they allow a robust modelling even in case of highly uncertain or incomplete datasets. Predictions from GP are dependent of the covariance kernel function selected to explain the data variance. To overcome this limitation, we propose a framework to identify the optimal covariance kernel function to model the data.The optimal kernel is defined as a composition of base kernel functions used to identify correlation patterns between data points. Our approach includes a modified version of the Compositional Kernel Learning (CKL) algorithm, in which we score the kernel families using a new energy function that depends both the Bayesian Information Criterion (BIC) and the explained variance score. We applied the proposed framework to model the progression of neurodegenerative diseases over time, in particular the progression of autosomal dominantly-inherited Alzheimer's disease, and use it to predict the time to clinical onset of subjects carrying genetic mutation.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Nicholas Petrick
ISBN (Electronic)9781510616394
StatePublished - 2018
EventMedical Imaging 2018: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 12 2018Feb 15 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2018: Computer-Aided Diagnosis
Country/TerritoryUnited States


  • Clinical onset prediction
  • Compositional kernel Learning
  • Covariance kernel functions
  • Disease progression model
  • Gaussian Process
  • Neurodegenerative Diseases


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