Learning to Rank from Medical Imaging Data

  • Fabian Pedregosa
  • Elodie Cauvet
  • Gaël Varoquaux
  • Christophe Pallier
  • Bertrand Thirion
  • Alexandre Gramfort
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Burges, C.J.C.: From RankNet to LambdaRank to LambdaMART: An overview. Learning 11(MSR-TR-2010-82), 23–581 (2010)Google Scholar
  2. 2.
    Carroll, M.K., Cecchi, G.A., Rish, I., Garg, R., Rao, A.R.: Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 44(1), 112–122 (2009)CrossRefGoogle Scholar
  3. 3.
    Cauvet, E.: Traitement des Structures Syntaxiques dans le langage et dans la musique. Ph.D. thesis, Ecole doctorale n158, Cerveau - Cognition - Comportement (2012)Google Scholar
  4. 4.
    Cuingnet, R., Rosso, C., Chupin, M., Lehéricy, S., Dormont, D., Benali, H., Samson, Y., Colliot, O.: Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome. Medical Image Analysis (2011)Google Scholar
  5. 5.
    Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: NIPS, pp. 155–161 (1996)Google Scholar
  6. 6.
    Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex. Science 293(5539), 2425–2430 (2001)CrossRefGoogle Scholar
  7. 7.
    Haynes, J.D., Rees, G.: Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7, 523 (2006)CrossRefGoogle Scholar
  8. 8.
    Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression, vol. 88, pp. 115–132. MIT Press, Cambridge (2000)Google Scholar
  9. 9.
    Jimura, K., Poldrack, R.A.: Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 1–9 (2011)Google Scholar
  10. 10.
    Joachims, T.: Training linear SVMs in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 217–226. ACM, New York (2006)Google Scholar
  11. 11.
    Kay, K.N., Naselaris, T., Prenger, R.J., Gallant, J.L.: Identifying natural images from human brain activity. Nature 452, 352–355 (2008)CrossRefGoogle Scholar
  12. 12.
    LaConte, S., Strother, S., Cherkassky, V., Anderson, J., Hu, X.: Support vector machines for temporal classification of block design fMRI data. NeuroImage 26(2), 317–329 (2005)CrossRefGoogle Scholar
  13. 13.
    Liu, H., Palatucci, M., Zhang, J.: Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 649–656. ACM, New York (2009)Google Scholar
  14. 14.
    Michel, V., Gramfort, A., Varoquaux, G., Eger, E., Thirion, B.: Total variation regularization for fMRI-based prediction of behaviour. IEEE Transactions on Medical Imaging 30(7), 1328–1340 (2011)CrossRefGoogle Scholar
  15. 15.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)Google Scholar
  16. 16.
    Richardson, M., Prakash, A., Brill, E.: Beyond PageRank: machine learning for static ranking. In: WWW 2006, pp. 707–715. ACM, New York (2006)Google Scholar
  17. 17.
    Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A.: The neural basis of loss aversion in decision-making under risk. Science 315(5811), 515–518 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fabian Pedregosa
    • 1
    • 2
    • 4
  • Elodie Cauvet
    • 3
    • 2
  • Gaël Varoquaux
    • 1
    • 2
  • Christophe Pallier
    • 3
    • 1
    • 2
  • Bertrand Thirion
    • 1
    • 2
  • Alexandre Gramfort
    • 1
    • 2
  1. 1.Parietal Team, INRIA Saclay-Île-de-FranceSaclayFrance
  2. 2.CEA, DSV, I2BMGif-Sur-YvetteFrance
  3. 3.Inserm, U992Gif-Sur-YvetteFrance
  4. 4.SIERRA Team, INRIA Paris - RocquencourtParisFrance

Personalised recommendations