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|
%global _empty_manifest_terminate_build 0
Name: python-miscnn
Version: 1.4.0
Release: 1
Summary: Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
License: GPLv3
URL: https://github.com/frankkramer-lab/MIScnn
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/3e/c6/3368ea5168d440a809264a0815beb9b26516c137a1fb106c3596e0f5125e/miscnn-1.4.0.tar.gz
BuildArch: noarch
Requires: python3-tensorflow
Requires: python3-tensorflow-addons
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-tqdm
Requires: python3-nibabel
Requires: python3-matplotlib
Requires: python3-pillow
Requires: python3-batchgenerators
Requires: python3-pydicom
Requires: python3-SimpleITK
Requires: python3-scikit-image
%description
[](https://www.python.org/)
[](https://travis-ci.org/github/frankkramer-lab/MIScnn)
[](https://codecov.io/gh/frankkramer-lab/miscnn)
[](https://pypi.org/project/miscnn/)
[](https://pypistats.org/packages/miscnn)
[](https://www.gnu.org/licenses/gpl-3.0.en.html)
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
## 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.
%package -n python3-miscnn
Summary: Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
Provides: python-miscnn
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-miscnn
[](https://www.python.org/)
[](https://travis-ci.org/github/frankkramer-lab/MIScnn)
[](https://codecov.io/gh/frankkramer-lab/miscnn)
[](https://pypi.org/project/miscnn/)
[](https://pypistats.org/packages/miscnn)
[](https://www.gnu.org/licenses/gpl-3.0.en.html)
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
## 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.
%package help
Summary: Development documents and examples for miscnn
Provides: python3-miscnn-doc
%description help
[](https://www.python.org/)
[](https://travis-ci.org/github/frankkramer-lab/MIScnn)
[](https://codecov.io/gh/frankkramer-lab/miscnn)
[](https://pypi.org/project/miscnn/)
[](https://pypistats.org/packages/miscnn)
[](https://www.gnu.org/licenses/gpl-3.0.en.html)
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
## 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.
%prep
%autosetup -n miscnn-1.4.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-miscnn -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.0-1
- Package Spec generated
|
