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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 - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
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.
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Submitted on : Sunday, September 30, 2012 - 2:11:59 PM
Last modification on : Thursday, March 17, 2022 - 10:08:43 AM
Long-term archiving on: : Friday, December 16, 2016 - 6:17:12 PM


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  • HAL Id : hal-00717990, version 2
  • ARXIV : 1207.3598



Fabian Pedregosa, Elodie Cauvet, Gaël Varoquaux, Christophe Pallier, Bertrand Thirion, et al.. Learning to rank from medical imaging data. Third International Workshop on Machine Learning in Medical Imaging - MLMI 2012, INRIA, Oct 2012, Nice, France. ⟨hal-00717990v2⟩



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