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Text to Brain: Predicting the Spatial Distribution of Neuroimaging Observations from Text Reports

  • Jérôme DockèsEmail author
  • Demian Wassermann
  • Russell Poldrack
  • Fabian Suchanek
  • Bertrand Thirion
  • Gaël Varoquaux
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)

Abstract

Despite the digital nature of magnetic resonance imaging, the resulting observations are most frequently reported and stored in text documents. There is a trove of information untapped in medical health records, case reports, and medical publications. In this paper, we propose to mine brain medical publications to learn the spatial distribution associated with anatomical terms. The problem is formulated in terms of minimization of a risk on distributions which leads to a least-deviation cost function. An efficient algorithm in the dual then learns the mapping from documents to brain structures. Empirical results using coordinates extracted from the brain-imaging literature show that (i) models must adapt to semantic variation in the terms used to describe a given anatomical structure, (ii) voxel-wise parameterization leads to higher likelihood of locations reported in unseen documents, (iii) least-deviation cost outperforms least-square. As a proof of concept for our method, we use our model of spatial distributions to predict the distribution of specific neurological conditions from text-only reports.

Notes

Acknowledgements

This project received funding from: the European Union’s H2020 Research Programme under Grant Agreement No. 785907 (HBP SGA2), the Metacog Digiteo project, the MetaMRI associate team, and ERC NeuroLang.

Supplementary material

473976_1_En_67_MOESM1_ESM.pdf (362 kb)
Supplementary material 1 (pdf 362 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jérôme Dockès
    • 1
  • Demian Wassermann
    • 1
  • Russell Poldrack
    • 2
  • Fabian Suchanek
    • 3
  • Bertrand Thirion
    • 1
  • Gaël Varoquaux
    • 1
  1. 1.INRIA, CEAUniversité Paris-SaclayParisFrance
  2. 2.Stanford UniversityStanfordUSA
  3. 3.Télécom ParisTechParisFrance

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