Classification of Alzheimer's disease patients and controls with Gaussian processes
Jonathan Young, Marc Modat, Manuel J Cardoso, John Ashburner, Sébastien Ourselin · 2012
There has been a great deal of recent work making use of multivariate classification techniques such as support vector machines to classify brain images obtained from MRI or PET as healthy or suffering from neurodegenerative disease. In the case of Alzheimer's disease, the results are as accurate as standard clinical tests and could potentially be used in a diagnostic setting. However these techniques give categorical class decisions. Here we show for the first time that Gaussian processes can be applied to structural neuroimaging data to perform classification of Alzheimer's disease subjects in a fully Bayesian framework. This offers advantages such as automatic setting of parameters via type II maximum likelihood and probabilistic predictions that may be useful in a clinical context, while maintaining the same accuracy as a state-of-the-art discriminative classifier applied to the same data.