Awesome Open Source
Awesome Open Source

DLTK Model Zoo

Gitter Build Status

DLTK Model Zoo logo

Referencing and citing methods in the Model Zoo

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}
}

Installation

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.

How to contribute

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 Gitter.

Basic contribution guidelines

  • Python coding style: Like TensorFlow, we loosely adhere to google coding style and google docstrings.
  • Entirely new features should be committed to dltk/contrib before 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 team

The DLTK Model Zoo is currently maintained by @pawni and @mrajchl, with greatly appreciated contributions from @baiwenjia @farrell236 (alphabetical order).

License

See LICENSE

Acknowledgments

We would like to thank NVIDIA GPU Computing for providing us with hardware for our research.


Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
jupyter-notebook (6,321
deep-learning (3,991
machine-learning (3,648
data-science (902
deep-neural-networks (468
cnn (403
medical-imaging (96
models (67
medical (43
medical-image-processing (28