%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 ![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png) [![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/) [![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn) [![shield_coverage](https://img.shields.io/codecov/c/gh/frankkramer-lab/miscnn?style=flat-square)](https://codecov.io/gh/frankkramer-lab/miscnn) [![shield_pypi_version](https://img.shields.io/pypi/v/miscnn?style=flat-square)](https://pypi.org/project/miscnn/) [![shield_pypi_downloads](https://img.shields.io/pypi/dm/miscnn?style=flat-square)](https://pypistats.org/packages/miscnn) [![shield_license](https://img.shields.io/github/license/frankkramer-lab/miscnn?style=flat-square)](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 ![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. %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 ![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png) [![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/) [![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn) [![shield_coverage](https://img.shields.io/codecov/c/gh/frankkramer-lab/miscnn?style=flat-square)](https://codecov.io/gh/frankkramer-lab/miscnn) [![shield_pypi_version](https://img.shields.io/pypi/v/miscnn?style=flat-square)](https://pypi.org/project/miscnn/) [![shield_pypi_downloads](https://img.shields.io/pypi/dm/miscnn?style=flat-square)](https://pypistats.org/packages/miscnn) [![shield_license](https://img.shields.io/github/license/frankkramer-lab/miscnn?style=flat-square)](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 ![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. %package help Summary: Development documents and examples for miscnn Provides: python3-miscnn-doc %description help ![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png) [![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/) [![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn) [![shield_coverage](https://img.shields.io/codecov/c/gh/frankkramer-lab/miscnn?style=flat-square)](https://codecov.io/gh/frankkramer-lab/miscnn) [![shield_pypi_version](https://img.shields.io/pypi/v/miscnn?style=flat-square)](https://pypi.org/project/miscnn/) [![shield_pypi_downloads](https://img.shields.io/pypi/dm/miscnn?style=flat-square)](https://pypistats.org/packages/miscnn) [![shield_license](https://img.shields.io/github/license/frankkramer-lab/miscnn?style=flat-square)](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 ![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. %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 - 1.4.0-1 - Package Spec generated