TY - GEN
T1 - Text to brain
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
AU - Dockès, Jérôme
AU - Wassermann, Demian
AU - Poldrack, Russell
AU - Suchanek, Fabian
AU - Thirion, Bertrand
AU - Varoquaux, Gaël
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-030-00931-1_67
DO - 10.1007/978-3-030-00931-1_67
M3 - Conference contribution
AN - SCOPUS:85053934910
SN - 9783030009304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 584
EP - 592
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Davatzikos, Christos
A2 - Fichtinger, Gabor
A2 - Alberola-López, Carlos
A2 - Schnabel, Julia A.
PB - Springer Verlag
Y2 - 16 September 2018 through 20 September 2018
ER -