To find out how to reference each implementation, please refer to the specifications in the authors' README.md. If you use DLTK in your work please refer to this citation:
@article{pawlowski2017state,
title={DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images},
author={Nick Pawlowski and S. Ira Ktena, and Matthew C.H. Lee and Bernhard Kainz and Daniel Rueckert and Ben Glocker and Martin Rajchl},
journal={arXiv preprint arXiv:1711.06853},
year={2017}
}
To install DLTK, check out the installation instructions on the main repo. Although not encouraged, additional dependecies might need to be installed for each separate model implementation. Please refer to the individual README.md files for further instructions. Other than that, clone the Model Zoo repository via
git clone https://github.com/DLTK/models.git
and download any pre-trained models, if available for download.
We appreciate any contributions to the DLTK and its Model Zoo. If you have improvements, features or patches, please send us your pull requests! You can find specific instructions on how to issue a PR on github here. Feel free to open an issue if you find a bug or directly come chat with us on our gitter channel
- Python coding style: Like TensorFlow, we loosely adhere to google coding style and google docstrings.
- Entirely new features should be committed to
dltk/contribbefore we can sensibly integrate it into the core. - Standalone problem-specific applications or (re-)implementations of published methods should be committed to the DLTK Model Zoo repo and provide a README.md file with author/coder contact information.
The DLTK Model Zoo is currently maintained by @pawni and @mrajchl, with greatly appreciated contributions from @baiwenjia @farrell236 (alphabetical order).
See LICENSE
We would like to thank NVIDIA GPU Computing for providing us with hardware for our research.