Section: New Results
Machine learning for Neuro-imaging
Participants : Fabian Pedregosa [correspondant] , Francis Bach, Guillaume Obozinski.
In the course of the year 2011-2012 two articles where submitted and accepted in international workshops. The first published article, Improved brain pattern recovery through ranking approaches ( ) was presented at the 2nd International Workshop on Pattern Recognition in NeuroImaging in London, July 2012 and proposes a new approach for the problem of estimating the coefficients of a generalized linear model with monotonicity constraint. For this, we explore the use of ranking techniques, which are popular in the context of information retrieval but novel for medical imaging applications.
The second published article, Learning to rank from medical imaging data ( ) uses the same techniques as the previous article to solve a more fundamental problem, that is, to predict a quantitative (and potentially non-linear) variable from a set of noisy measurements. We show on simulations and two fMRI datasets that this approach is able to predict the correct ordering on pairs of images, yielding higher prediction accuracy than standard regression and multiclass classification techniques.
Collaboration with the Parietal project-team (A. Gramfort, B. Thirion, G. Varoquaux)