Learning to rank from medical imaging data

Fabian Pedregosa; Elodie Cauvet; Gaël Varoquaux; Christophe Pallier; Bertrand Thirion; Alexandre Gramfort

Learning to rank from medical imaging data

Fabian Pedregosa ^{1, 2} Elodie Cauvet ³ Gaël Varoquaux ^{1, 4} Christophe Pallier ^{1, 3} Bertrand Thirion ¹ Alexandre Gramfort ^{1, 4}

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France

2 SIERRA - Statistical Machine Learning and Parsimony

DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548

3 NEUROSPIN - Service NEUROSPIN

4 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur

Abstract : Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning procedure where the model aims to order or rank images. We use a linear model for its robustness in high dimension and its possible interpretation. 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.

Keywords : fMRI supervised learning decoding ranking

Document type :

Conference papers

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

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https://hal.inria.fr/hal-00717990
Contributor : Fabian Pedregosa <>
Submitted on : Sunday, September 30, 2012 - 2:11:59 PM
Last modification on : Thursday, March 7, 2019 - 3:34:14 PM
Document(s) archivé(s) le : Friday, December 16, 2016 - 6:17:12 PM

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