Learning to ground medical text in a 3D human atlas
Dusan Grujicic, Gorjan Radevski, Tinne Tuytelaars, Matthew Blaschko
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.- Anthology ID:
- 2020.conll-1.23
- Volume:
- Proceedings of the 24th Conference on Computational Natural Language Learning
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Raquel Fernández, Tal Linzen
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 302–312
- Language:
- URL:
- https://aclanthology.org/2020.conll-1.23
- DOI:
- 10.18653/v1/2020.conll-1.23
- Cite (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.
- Cite (Informal):
- Learning to ground medical text in a 3D human atlas (Grujicic et al., CoNLL 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.conll-1.23.pdf
- Code
- gorjanradevski/text2atlas
- Data
- BioASQ