Metadata-Version: 2.1
Name: miscnn
Version: 1.0.1
Summary: Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
Home-page: https://github.com/frankkramer-lab/MIScnn
Author: Dominik Müller
Author-email: dominik.mueller@informatik.uni-augsburg.de
License: GPLv3
Description: ![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png)
        
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        The 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.
        
        **MIScnn provides several core features:**
        - 2D/3D medical image segmentation for binary and multi-class problems
        - Data I/O, preprocessing and data augmentation for biomedical images
        - Patch-wise and full image analysis
        - State-of-the-art deep learning model and metric library
        - Intuitive and fast model utilization (training, prediction)
        - Multiple automatic evaluation techniques (e.g. cross-validation)
        - Custom model, data I/O, pre-/postprocessing and metric support
        - Based on Keras with Tensorflow as backend
        
        ![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/MIScnn.pipeline.png)
        
        ## Resources
        
        - MIScnn Documentation: [GitHub wiki - Home](https://github.com/frankkramer-lab/MIScnn/wiki)
        - MIScnn Tutorials: [Overview of Tutorials](https://github.com/frankkramer-lab/MIScnn/wiki/Tutorials)
        - MIScnn Examples: [Overview of Use Cases and Examples](https://github.com/frankkramer-lab/MIScnn/wiki/Examples)
        - MIScnn Development Tracker: [GitHub project - MIScnn Development](https://github.com/frankkramer-lab/MIScnn/projects/1)
        - MIScnn on GitHub: [GitHub - frankkramer-lab/MIScnn](https://github.com/frankkramer-lab/MIScnn)
        - MIScnn on PyPI: [PyPI - miscnn](https://pypi.org/project/miscnn/)
        
        ## Author
        
        Dominik Müller  
        Email: dominik.mueller@informatik.uni-augsburg.de  
        IT-Infrastructure for Translational Medical Research  
        University Augsburg  
        Augsburg, Bavaria, Germany
        
        ## How to cite / More information
        
        Dominik Müller and Frank Kramer. (2019)  
        MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning.  
        arXiv e-print: [https://arxiv.org/abs/1910.09308](https://arxiv.org/abs/1910.09308)
        
        ```
        Article{miscnn,
          title={MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning},
          author={Dominik Müller and Frank Kramer},
          year={2019},
          eprint={1910.09308},
          archivePrefix={arXiv},
          primaryClass={eess.IV}
        }
        ```
        
        Thank you for citing our work.
        
        ## License
        
        This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\
        See the LICENSE.md file for license rights and limitations.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.6
Description-Content-Type: text/markdown
