Learning to ground medical text in a 3D human atlas

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This repository is the official implementation of Learning to ground medical text in a 3D human atlas (published at CoNLL 2020) authored by Dusan Grujicic^*, Gorjan Radevski^*, Tinne Tuytelaars and Matthew Blaschko .

^* Equal contribution

Requirements

If you are using Poetry, navigating to the project root directory and running poetry install will suffice. Otherwise, a requirements.txt file is present so you can install all dependencies by running pip install -r requirements.txt. However, if you just want to download the trained models or dataset splits, make sure to have gdown installed. If the project dependencies are installed then gdown is already present. Otherwise, run pip install gdown to install it.

Datasets and models

Will be released shortly.

Reference

If you found this code useful, or use some of our resources for your work, we will appreciate if you cite our paper.

@inproceedings{Grujicic2020b,
  title={Learning to ground medical text in a {3D} human atlas},
  AUTHOR = {Grujicic, D. and G. Radevski and T. Tuytelaars and M. B. Blaschko},
  YEAR = {2020},
  booktitle= {The SIGNLL Conference on Computational Natural Language Learning},
}

License

Everything is licensed under the MIT License.