| Name | pyradiomics-cuda JSON |
| Version |
1.0.3
JSON |
| download |
| home_page | None |
| Summary | CUDA-accelerated fork of PyRadiomics - a Radiomics features library for python |
| upload_time | 2025-04-20 21:56:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.5 |
| license | Copyright 2025 Piotr Tyrakowski, Jakub Lisowski - CUDA extensions and modifications
Copyright 2017 Harvard Medical School - Original PyRadiomics
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
| keywords |
radiomics
cancerimaging
medicalresearch
computationalimaging
cuda
gpu
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
SimpleITK
PyWavelets
pykwalify
six
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pyradiomics-cuda v1.0.3
## CUDA-Accelerated Fork
This is a CUDA-accelerated fork of the original [PyRadiomics package](https://github.com/AIM-Harvard/pyradiomics) (based on version 3.0.1). It provides GPU acceleration for computationally intensive tasks, significantly improving performance when a CUDA-compatible GPU is available.
This fork is distributed under the 3-clause BSD license (see LICENSE file).
### Performance Benefits
The CUDA acceleration focuses primarily on shape features calculation, which can be computationally expensive in the original PyRadiomics, especially for complex 3D volumes. Depending on your hardware and the size of your datasets, you can expect:
- Up to 10-50x speedup on shape feature computation with a modern NVIDIA GPU
- Automatic fallback to CPU when CUDA is not available
- Same results and precision as the original implementation
### Requirements
**Runtime Requirements:**
- CUDA-compatible NVIDIA GPU
- NVIDIA CUDA Runtime libraries compatible with the toolkit used for compilation (tested with 12.x driver)
- All the original PyRadiomics Python dependencies (numpy, SimpleITK, PyWavelets, pykwalify, six) - these will be installed automatically by pip.
**Build Requirements (for installing from source):**
Since this package includes C and CUDA extensions, installing from the source distribution (`.tar.gz`) requires compilation on your machine. You **must** have the following installed and configured **before** running `pip install pyradiomics-cuda`:
- **A C Compiler:** Such as GCC on Linux, or Microsoft Visual C++ Build Tools on Windows.
- **Python Development Headers:** Necessary for compiling C extensions.
- On Debian/Ubuntu: `sudo apt-get update && sudo apt-get install python3-dev`
- On Fedora/CentOS: `sudo yum update && sudo yum install python3-devel`
- On Windows: Usually included with Python installation or Visual Studio.
- **NVIDIA CUDA Toolkit:** The full toolkit including the `nvcc` compiler is required.
- Tested with CUDA Toolkit 12.x.
- Ensure the CUDA installation directory (containing `nvcc`) is added to your system's `PATH` environment variable.
- **GPU Compute Capability:** Your NVIDIA GPU must have a Compute Capability of 6.0 or higher due to the use of double-precision atomic operations.
### Installation
```bash
pip install pyradiomics-cuda
```
If you want to disable CUDA extensions during installation, use:
```bash
DISABLE_CUDA_EXTENSIONS=1 pip install pyradiomics-cuda
```
### Usage
This package can be used exactly the same way as the original PyRadiomics, but will automatically use GPU acceleration when a compatible CUDA device is available:
```python
import radiomics
from radiomics import featureextractor
# The usage is identical to the original PyRadiomics
extractor = featureextractor.RadiomicsFeatureExtractor()
result = extractor.execute(imageFilepath, maskFilepath)
```
### Authors
- Piotr Tyrakowski ([@PiotrTyrakowski](https://github.com/PiotrTyrakowski))
- Jakub Lisowski ([@Jlisowskyy](https://github.com/Jlisowskyy))
## Original PyRadiomics Information
**Note:** The following information pertains to the original `pyradiomics` package. Some details, particularly regarding installation, Docker images, and build status, may differ for this CUDA-accelerated fork.
# pyradiomics v3.1.0
## Build Status
| Linux / MacOS | Windows |
| ----------------------------- | ----------------------------- |
| [![][circleci]][circleci-lnk] | [![][appveyor]][appveyor-lnk] |
[appveyor]: https://ci.appveyor.com/api/projects/status/kvu7897q0v4imwdc?svg=true
[appveyor-lnk]: https://ci.appveyor.com/project/AIM-Harvard/pyradiomics-k4sto
[circleci]: https://dl.circleci.com/status-badge/img/gh/AIM-Harvard/pyradiomics/tree/master.svg?style=shield
[circleci-lnk]: https://circleci.com/gh/AIM-Harvard/pyradiomics
## Radiomics feature extraction in Python
This is an open-source python package for the extraction of Radiomics features from medical imaging.
With this package we aim to establish a reference standard for Radiomic Analysis, and provide a tested and maintained
open-source platform for easy and reproducible Radiomic Feature extraction. By doing so, we hope to increase awareness
of radiomic capabilities and expand the community.
The platform supports both the feature extraction in 2D and 3D and can be used to calculate single values per feature
for a region of interest ("segment-based") or to generate feature maps ("voxel-based").
**Not intended for clinical use.**
**If you publish any work which uses this package, please cite the following publication:**
*van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H.,
Fillion-Robin, J. C., Pieper, S., Aerts, H. J. W. L. (2017). Computational Radiomics System to Decode the Radiographic
Phenotype. Cancer Research, 77(21), e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339*
### Join the Community!
For questions specifically about this **CUDA fork**, please use the [GitHub Issues](https://github.com/PiotrTyrakowski/pyradiomics-CUDA/issues) for this repository.
For general questions about **PyRadiomics concepts or the original package**, please join the [Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).
### Feature Classes
Currently supports the following feature classes:
- First Order Statistics
- Shape-based (2D and 3D)
- Gray Level Co-occurrence Matrix (GLCM)
- Gray Level Run Length Matrix (GLRLM)
- Gray Level Size Zone Matrix (GLSZM)
- Gray Level Dependece Matrix (GLDM)
- Neighboring Gray Tone Difference Matrix (NGTDM)
### Filter Classes
Aside from the feature classes, there are also some built-in optional filters:
- Laplacian of Gaussian (LoG, based on SimpleITK functionality)
- Wavelet (using the PyWavelets package)
- Square
- Square Root
- Logarithm
- Exponential
- Gradient (Magnitude)
- Local Binary Pattern (LBP) 2D / 3D
### Supporting reproducible extraction
Aside from calculating features, the pyradiomics package includes provenance information in the
output. This information contains information on used image and mask, as well as applied settings
and filters, thereby enabling fully reproducible feature extraction.
### Documentation
For more information, see the sphinx generated documentation available [here](http://pyradiomics.readthedocs.io/).
Alternatively, you can generate the documentation by checking out the master branch and running from the root directory:
python setup.py build_sphinx
The documentation can then be viewed in a browser by opening `PACKAGE_ROOT\build\sphinx\html\index.html`.
Furthermore, an instruction video is available [here](http://radiomics.io/pyradiomics.html).
### Installation
PyRadiomics is OS independent and compatible with Python >= 3.5. Pre-built binaries are available on
PyPi and Conda. To install PyRadiomics, ensure you have python
installed and run:
`python -m pip install pyradiomics`
Detailed installation instructions, as well as instructions for building PyRadiomics from source, are available in the
[documentation](http://pyradiomics.readthedocs.io/en/latest/installation.html).
### Docker
**Note:** The following Docker images are for the **original `pyradiomics` package** and **do not** include the CUDA accelerations from this fork.
PyRadiomics also supports [Dockers](https://www.docker.com/). Currently, 2 dockers are available:
The first one is a [Jupyter notebook](http://jupyter.org/) with PyRadiomics pre-installed with example Notebooks.
To get the Docker:
docker pull radiomics/pyradiomics:latest
The `radiomics/notebook` Docker has an exposed volume (`/data`) that can be mapped to the host system directory. For example, to mount the current directory:
docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook
or for a less secure notebook, skip the randomly generated token
docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook start-notebook.sh --NotebookApp.token=''
and open the local webpage at http://localhost:8888/ with the current directory at http://localhost:8888/tree/data.
The second is a docker which exposes the PyRadiomics CLI interface. To get the CLI-Docker:
docker pull radiomics/pyradiomics:CLI
You can then use the PyRadiomics CLI as follows:
docker run radiomics/pyradiomics:CLI --help
For more information on using docker, see
[here](https://pyradiomics.readthedocs.io/en/latest/installation.html#use-pyradiomics-docker)
### Usage
PyRadiomics can be easily used in a Python script through the `featureextractor`
module. Furthermore, PyRadiomics provides a commandline script, `pyradiomics`, for both single image extraction and
batchprocessing. Finally, a convenient front-end interface is provided as the 'Radiomics'
extension for 3D Slicer, available [here](https://github.com/AIM-Harvard/SlicerRadiomics).
### 3rd-party packages used in pyradiomics:
- SimpleITK (Image loading and preprocessing)
- numpy (Feature calculation)
- PyWavelets (Wavelet filter)
- pykwalify (Enabling yaml parameters file checking)
- six (Python 3 Compatibility)
- scipy (Only for LBP filter, install separately to enable this filter)
- scikit-image (Only for LBP filter, install separately to enable this filter)
- trimesh (Only for LBP filter, install separately to enable this filter)
See also the [requirements file](requirements.txt).
### 3D Slicer
PyRadiomics is also available as an [extension](https://github.com/AIM-Harvard/SlicerRadiomics) to [3D Slicer](slicer.org).
Download and install the 3D slicer [nightly build](http://download.slicer.org/), the extension is then available in the
extension manager under "SlicerRadiomics".
### License
This package is covered by the open source [3-clause BSD License](LICENSE.txt).
### Developers
- [Joost van Griethuysen](https://github.com/JoostJM)<sup>1,3,4</sup>
- [Andriy Fedorov](https://github.com/fedorov)<sup>2</sup>
- [Nicole Aucoin](https://github.com/naucoin)<sup>2</sup>
- [Jean-Christophe Fillion-Robin](https://github.com/jcfr)<sup>5</sup>
- [Ahmed Hosny](https://github.com/ahmedhosny)<sup>1</sup>
- [Steve Pieper](https://github.com/pieper)<sup>6</sup>
- [Hugo Aerts (PI)](https://github.com/hugoaerts)<sup>1,2</sup>
<sup>1</sup>Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA,
<sup>2</sup>Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA,
<sup>3</sup>Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands,
<sup>4</sup>GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands,
<sup>5</sup>Kitware,
<sup>6</sup>Isomics
### Contact
We are happy to help you with any questions.
- For questions about this **CUDA fork**, please use the [GitHub Issues](https://github.com/PiotrTyrakowski/pyradiomics-CUDA/issues).
- For general **PyRadiomics** questions, please use the [Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).
We welcome contributions to PyRadiomics. Please read the [contributing guidelines](CONTRIBUTING.rst) on how to
contribute to PyRadiomics.
**This work was supported in part by the US National Cancer Institute grants:
U24CA194354 - QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE and U01CA190234 - TUMOR GENOTYPE AND RADIOMIC PHENOTYPE IN LUNG CANCER**
Raw data
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"description": "# pyradiomics-cuda v1.0.3\n\n## CUDA-Accelerated Fork\n\nThis is a CUDA-accelerated fork of the original [PyRadiomics package](https://github.com/AIM-Harvard/pyradiomics) (based on version 3.0.1). It provides GPU acceleration for computationally intensive tasks, significantly improving performance when a CUDA-compatible GPU is available.\n\nThis fork is distributed under the 3-clause BSD license (see LICENSE file).\n\n### Performance Benefits\n\nThe CUDA acceleration focuses primarily on shape features calculation, which can be computationally expensive in the original PyRadiomics, especially for complex 3D volumes. Depending on your hardware and the size of your datasets, you can expect:\n\n- Up to 10-50x speedup on shape feature computation with a modern NVIDIA GPU\n- Automatic fallback to CPU when CUDA is not available\n- Same results and precision as the original implementation\n\n### Requirements\n\n**Runtime Requirements:**\n\n- CUDA-compatible NVIDIA GPU\n- NVIDIA CUDA Runtime libraries compatible with the toolkit used for compilation (tested with 12.x driver)\n- All the original PyRadiomics Python dependencies (numpy, SimpleITK, PyWavelets, pykwalify, six) - these will be installed automatically by pip.\n\n**Build Requirements (for installing from source):**\n\nSince this package includes C and CUDA extensions, installing from the source distribution (`.tar.gz`) requires compilation on your machine. You **must** have the following installed and configured **before** running `pip install pyradiomics-cuda`:\n\n- **A C Compiler:** Such as GCC on Linux, or Microsoft Visual C++ Build Tools on Windows.\n- **Python Development Headers:** Necessary for compiling C extensions.\n - On Debian/Ubuntu: `sudo apt-get update && sudo apt-get install python3-dev`\n - On Fedora/CentOS: `sudo yum update && sudo yum install python3-devel`\n - On Windows: Usually included with Python installation or Visual Studio.\n- **NVIDIA CUDA Toolkit:** The full toolkit including the `nvcc` compiler is required. \n - Tested with CUDA Toolkit 12.x. \n - Ensure the CUDA installation directory (containing `nvcc`) is added to your system's `PATH` environment variable.\n- **GPU Compute Capability:** Your NVIDIA GPU must have a Compute Capability of 6.0 or higher due to the use of double-precision atomic operations.\n\n### Installation\n\n```bash\npip install pyradiomics-cuda\n```\n\nIf you want to disable CUDA extensions during installation, use:\n\n```bash\nDISABLE_CUDA_EXTENSIONS=1 pip install pyradiomics-cuda\n```\n\n### Usage\n\nThis package can be used exactly the same way as the original PyRadiomics, but will automatically use GPU acceleration when a compatible CUDA device is available:\n\n```python\nimport radiomics\nfrom radiomics import featureextractor\n\n# The usage is identical to the original PyRadiomics\nextractor = featureextractor.RadiomicsFeatureExtractor()\nresult = extractor.execute(imageFilepath, maskFilepath)\n```\n\n### Authors\n\n- Piotr Tyrakowski ([@PiotrTyrakowski](https://github.com/PiotrTyrakowski))\n- Jakub Lisowski ([@Jlisowskyy](https://github.com/Jlisowskyy))\n\n## Original PyRadiomics Information\n\n**Note:** The following information pertains to the original `pyradiomics` package. Some details, particularly regarding installation, Docker images, and build status, may differ for this CUDA-accelerated fork.\n\n# pyradiomics v3.1.0\n\n## Build Status\n\n| Linux / MacOS | Windows |\n| ----------------------------- | ----------------------------- |\n| [![][circleci]][circleci-lnk] | [![][appveyor]][appveyor-lnk] |\n\n\n[appveyor]: https://ci.appveyor.com/api/projects/status/kvu7897q0v4imwdc?svg=true\n[appveyor-lnk]: https://ci.appveyor.com/project/AIM-Harvard/pyradiomics-k4sto\n\n[circleci]: https://dl.circleci.com/status-badge/img/gh/AIM-Harvard/pyradiomics/tree/master.svg?style=shield\n[circleci-lnk]: https://circleci.com/gh/AIM-Harvard/pyradiomics\n\n## Radiomics feature extraction in Python\nThis is an open-source python package for the extraction of Radiomics features from medical imaging.\n\nWith this package we aim to establish a reference standard for Radiomic Analysis, and provide a tested and maintained\nopen-source platform for easy and reproducible Radiomic Feature extraction. By doing so, we hope to increase awareness\nof radiomic capabilities and expand the community.\n\nThe platform supports both the feature extraction in 2D and 3D and can be used to calculate single values per feature\nfor a region of interest (\"segment-based\") or to generate feature maps (\"voxel-based\"). \n\n**Not intended for clinical use.**\n\n**If you publish any work which uses this package, please cite the following publication:**\n*van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H.,\nFillion-Robin, J. C., Pieper, S., Aerts, H. J. W. L. (2017). Computational Radiomics System to Decode the Radiographic\nPhenotype. Cancer Research, 77(21), e104\u2013e107. https://doi.org/10.1158/0008-5472.CAN-17-0339*\n\n### Join the Community!\n\nFor questions specifically about this **CUDA fork**, please use the [GitHub Issues](https://github.com/PiotrTyrakowski/pyradiomics-CUDA/issues) for this repository.\n\nFor general questions about **PyRadiomics concepts or the original package**, please join the [Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).\n\n### Feature Classes\nCurrently supports the following feature classes:\n\n - First Order Statistics\n - Shape-based (2D and 3D)\n - Gray Level Co-occurrence Matrix (GLCM)\n - Gray Level Run Length Matrix (GLRLM)\n - Gray Level Size Zone Matrix (GLSZM)\n - Gray Level Dependece Matrix (GLDM)\n - Neighboring Gray Tone Difference Matrix (NGTDM)\n\n### Filter Classes\nAside from the feature classes, there are also some built-in optional filters:\n\n- Laplacian of Gaussian (LoG, based on SimpleITK functionality)\n- Wavelet (using the PyWavelets package)\n- Square\n- Square Root\n- Logarithm\n- Exponential\n- Gradient (Magnitude)\n- Local Binary Pattern (LBP) 2D / 3D\n\n### Supporting reproducible extraction\nAside from calculating features, the pyradiomics package includes provenance information in the\noutput. This information contains information on used image and mask, as well as applied settings\nand filters, thereby enabling fully reproducible feature extraction.\n\n### Documentation\nFor more information, see the sphinx generated documentation available [here](http://pyradiomics.readthedocs.io/).\n\nAlternatively, you can generate the documentation by checking out the master branch and running from the root directory:\n\n python setup.py build_sphinx\n\nThe documentation can then be viewed in a browser by opening `PACKAGE_ROOT\\build\\sphinx\\html\\index.html`. \n\nFurthermore, an instruction video is available [here](http://radiomics.io/pyradiomics.html).\n\n### Installation\nPyRadiomics is OS independent and compatible with Python >= 3.5. Pre-built binaries are available on\nPyPi and Conda. To install PyRadiomics, ensure you have python\ninstalled and run:\n\n `python -m pip install pyradiomics`\n\nDetailed installation instructions, as well as instructions for building PyRadiomics from source, are available in the \n[documentation](http://pyradiomics.readthedocs.io/en/latest/installation.html).\n\n### Docker\n\n**Note:** The following Docker images are for the **original `pyradiomics` package** and **do not** include the CUDA accelerations from this fork.\n\nPyRadiomics also supports [Dockers](https://www.docker.com/). Currently, 2 dockers are available:\n\nThe first one is a [Jupyter notebook](http://jupyter.org/) with PyRadiomics pre-installed with example Notebooks. \n\nTo get the Docker:\n\n docker pull radiomics/pyradiomics:latest\n\nThe `radiomics/notebook` Docker has an exposed volume (`/data`) that can be mapped to the host system directory. For example, to mount the current directory:\n\n docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook\n\nor for a less secure notebook, skip the randomly generated token\n\n docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook start-notebook.sh --NotebookApp.token=''\n\nand open the local webpage at http://localhost:8888/ with the current directory at http://localhost:8888/tree/data.\n\nThe second is a docker which exposes the PyRadiomics CLI interface. To get the CLI-Docker:\n\n docker pull radiomics/pyradiomics:CLI\n\nYou can then use the PyRadiomics CLI as follows:\n\n docker run radiomics/pyradiomics:CLI --help\n\nFor more information on using docker, see\n[here](https://pyradiomics.readthedocs.io/en/latest/installation.html#use-pyradiomics-docker)\n\n### Usage\nPyRadiomics can be easily used in a Python script through the `featureextractor`\nmodule. Furthermore, PyRadiomics provides a commandline script, `pyradiomics`, for both single image extraction and \nbatchprocessing. Finally, a convenient front-end interface is provided as the 'Radiomics'\nextension for 3D Slicer, available [here](https://github.com/AIM-Harvard/SlicerRadiomics).\n\n### 3rd-party packages used in pyradiomics:\n - SimpleITK (Image loading and preprocessing)\n - numpy (Feature calculation)\n - PyWavelets (Wavelet filter)\n - pykwalify (Enabling yaml parameters file checking)\n - six (Python 3 Compatibility)\n - scipy (Only for LBP filter, install separately to enable this filter)\n - scikit-image (Only for LBP filter, install separately to enable this filter)\n - trimesh (Only for LBP filter, install separately to enable this filter)\n\nSee also the [requirements file](requirements.txt).\n\n### 3D Slicer\nPyRadiomics is also available as an [extension](https://github.com/AIM-Harvard/SlicerRadiomics) to [3D Slicer](slicer.org). \nDownload and install the 3D slicer [nightly build](http://download.slicer.org/), the extension is then available in the\nextension manager under \"SlicerRadiomics\".\n\n### License\nThis package is covered by the open source [3-clause BSD License](LICENSE.txt).\n\n### Developers\n - [Joost van Griethuysen](https://github.com/JoostJM)<sup>1,3,4</sup>\n - [Andriy Fedorov](https://github.com/fedorov)<sup>2</sup>\n - [Nicole Aucoin](https://github.com/naucoin)<sup>2</sup>\n - [Jean-Christophe Fillion-Robin](https://github.com/jcfr)<sup>5</sup>\n - [Ahmed Hosny](https://github.com/ahmedhosny)<sup>1</sup>\n - [Steve Pieper](https://github.com/pieper)<sup>6</sup>\n - [Hugo Aerts (PI)](https://github.com/hugoaerts)<sup>1,2</sup>\n \n<sup>1</sup>Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA,\n<sup>2</sup>Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA,\n<sup>3</sup>Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands, \n<sup>4</sup>GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands,\n<sup>5</sup>Kitware,\n<sup>6</sup>Isomics\n\n### Contact\n\nWe are happy to help you with any questions. \n- For questions about this **CUDA fork**, please use the [GitHub Issues](https://github.com/PiotrTyrakowski/pyradiomics-CUDA/issues).\n- For general **PyRadiomics** questions, please use the [Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).\n\nWe welcome contributions to PyRadiomics. Please read the [contributing guidelines](CONTRIBUTING.rst) on how to\ncontribute to PyRadiomics.\n\n**This work was supported in part by the US National Cancer Institute grants: \nU24CA194354 - QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE and U01CA190234 - TUMOR GENOTYPE AND RADIOMIC PHENOTYPE IN LUNG CANCER**\n",
"bugtrack_url": null,
"license": "Copyright 2025 Piotr Tyrakowski, Jakub Lisowski - CUDA extensions and modifications\n Copyright 2017 Harvard Medical School - Original PyRadiomics\n \n Redistribution and use in source and binary forms, with or without modification, are permitted provided that the\n following conditions are met:\n \n 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following\n disclaimer.\n \n 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following\n disclaimer in the documentation and/or other materials provided with the distribution.\n \n 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products\n derived from this software without specific prior written permission.\n \n THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES,\n INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF\n THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n ",
"summary": "CUDA-accelerated fork of PyRadiomics - a Radiomics features library for python",
"version": "1.0.3",
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