Text to brain: predicting the spatial distribution of neuroimaging observations from text reports

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.
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Communication dans un congrès
MICCAI 2018 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2018, Granada, Spain. pp.1-18, 2018
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https://hal.archives-ouvertes.fr/hal-01807295
Contributeur : Jérôme Dockès <>
Soumis le : jeudi 28 juin 2018 - 11:11:20
Dernière modification le : samedi 30 juin 2018 - 01:18:48
Document(s) archivé(s) le : mercredi 26 septembre 2018 - 23:20:41

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  • HAL Id : hal-01807295, version 3
  • ARXIV : 1806.01139

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Jérôme Dockès, Demian Wassermann, Russell Poldrack, Fabian Suchanek, Bertrand Thirion, et al.. Text to brain: predicting the spatial distribution of neuroimaging observations from text reports. MICCAI 2018 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2018, Granada, Spain. pp.1-18, 2018. 〈hal-01807295v3〉

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