@inproceedings{grujicic-etal-2020-learning,
title = "Learning to ground medical text in a 3{D} human atlas",
author = "Grujicic, Dusan and
Radevski, Gorjan and
Tuytelaars, Tinne and
Blaschko, Matthew",
editor = "Fern{\'a}ndez, Raquel and
Linzen, Tal",
booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.conll-1.23",
doi = "10.18653/v1/2020.conll-1.23",
pages = "302--312",
abstract = "In this paper, we develop a method for grounding medical text into a physically meaningful and interpretable space corresponding to a human atlas. We build on text embedding architectures such as Bert and introduce a loss function that allows us to reason about the semantic and spatial relatedness of medical texts by learning a projection of the embedding into a 3D space representing the human body. We quantitatively and qualitatively demonstrate that our proposed method learns a context sensitive and spatially aware mapping, in both the inter-organ and intra-organ sense, using a large scale medical text dataset from the {``}Large-scale online biomedical semantic indexing{''} track of the 2020 BioASQ challenge. We extend our approach to a self-supervised setting, and find it to be competitive with a classification based method, and a fully supervised variant of approach.",
}
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%0 Conference Proceedings
%T Learning to ground medical text in a 3D human atlas
%A Grujicic, Dusan
%A Radevski, Gorjan
%A Tuytelaars, Tinne
%A Blaschko, Matthew
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F grujicic-etal-2020-learning
%X In this paper, we develop a method for grounding medical text into a physically meaningful and interpretable space corresponding to a human atlas. We build on text embedding architectures such as Bert and introduce a loss function that allows us to reason about the semantic and spatial relatedness of medical texts by learning a projection of the embedding into a 3D space representing the human body. We quantitatively and qualitatively demonstrate that our proposed method learns a context sensitive and spatially aware mapping, in both the inter-organ and intra-organ sense, using a large scale medical text dataset from the “Large-scale online biomedical semantic indexing” track of the 2020 BioASQ challenge. We extend our approach to a self-supervised setting, and find it to be competitive with a classification based method, and a fully supervised variant of approach.
%R 10.18653/v1/2020.conll-1.23
%U https://aclanthology.org/2020.conll-1.23
%U https://doi.org/10.18653/v1/2020.conll-1.23
%P 302-312
Markdown (Informal)
[Learning to ground medical text in a 3D human atlas](https://aclanthology.org/2020.conll-1.23) (Grujicic et al., CoNLL 2020)
ACL
- Dusan Grujicic, Gorjan Radevski, Tinne Tuytelaars, and Matthew Blaschko. 2020. Learning to ground medical text in a 3D human atlas. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 302–312, Online. Association for Computational Linguistics.