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            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Scientific/Engineering :: Medical Science Apps."
        ],
        "description": "![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png)\n\n[![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/)\n[![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn)\n[![shield_coverage](https://img.shields.io/codecov/c/gh/frankkramer-lab/miscnn?style=flat-square)](https://codecov.io/gh/frankkramer-lab/miscnn)\n[![shield_pypi_version](https://img.shields.io/pypi/v/miscnn?style=flat-square)](https://pypi.org/project/miscnn/)\n[![shield_pypi_downloads](https://img.shields.io/pypi/dm/miscnn?style=flat-square)](https://pypistats.org/packages/miscnn)\n[![shield_license](https://img.shields.io/github/license/frankkramer-lab/miscnn?style=flat-square)](https://www.gnu.org/licenses/gpl-3.0.en.html)\n\n\nThe open-source Python library MIScnn is an intuitive API allowing fast setup of medical image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.\n\n**MIScnn provides several core features:**\n- 2D/3D medical image segmentation for binary and multi-class problems\n- Data I/O, preprocessing and data augmentation for biomedical images\n- Patch-wise and full image analysis\n- State-of-the-art deep learning model and metric library\n- Intuitive and fast model utilization (training, prediction)\n- Multiple automatic evaluation techniques (e.g. cross-validation)\n- Custom model, data I/O, pre-/postprocessing and metric support\n- Based on Keras with Tensorflow as backend\n\n![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/MIScnn.pipeline.png)\n\n## Resources\n\n- MIScnn Documentation: [GitHub wiki - Home](https://github.com/frankkramer-lab/MIScnn/wiki)\n- MIScnn Tutorials: [Overview of Tutorials](https://github.com/frankkramer-lab/MIScnn/wiki/Tutorials)\n- MIScnn Examples: [Overview of Use Cases and Examples](https://github.com/frankkramer-lab/MIScnn/wiki/Examples)\n- MIScnn Development Tracker: [GitHub project - MIScnn Development](https://github.com/frankkramer-lab/MIScnn/projects/1)\n- MIScnn on GitHub: [GitHub - frankkramer-lab/MIScnn](https://github.com/frankkramer-lab/MIScnn)\n- MIScnn on PyPI: [PyPI - miscnn](https://pypi.org/project/miscnn/)\n\n## Author\n\nDominik M\u00fcller  \nEmail: dominik.mueller@informatik.uni-augsburg.de  \nIT-Infrastructure for Translational Medical Research  \nUniversity Augsburg  \nAugsburg, Bavaria, Germany\n\n## How to cite / More information\n\nDominik M\u00fcller and Frank Kramer. (2019)  \nMIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning.  \narXiv e-print: [https://arxiv.org/abs/1910.09308](https://arxiv.org/abs/1910.09308)\n\n```\nArticle{miscnn,\n  title={MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning},\n  author={Dominik M\u00fcller and Frank Kramer},\n  year={2019},\n  eprint={1910.09308},\n  archivePrefix={arXiv},\n  primaryClass={eess.IV}\n}\n```\n\nThank you for citing our work.\n\n## License\n\nThis project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\\\nSee the LICENSE.md file for license rights and limitations.\n\n\n",
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