aucmedi


Nameaucmedi JSON
Version 0.11.0 PyPI version JSON
download
home_pagehttps://frankkramer-lab.github.io/aucmedi/
SummaryAUCMEDI - a framework for Automated Classification of Medical Images
upload_time2025-07-21 14:52:43
maintainerNone
docs_urlNone
authorDominik Müller
requires_python>=3.9
licenseGPLv3
keywords
VCS
bugtrack_url
requirements tensorflow numpy pillow albumentations pandas scikit-learn scikit-image lime pooch classification-models-3D Keras-Applications SimpleITK batchgenerators volumentations-aucmedi plotnine pathos
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            ![aucmedi_logo](https://raw.githubusercontent.com/frankkramer-lab/aucmedi/master/docs/images/aucmedi.logo.description.png)

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The open-source software AUCMEDI allows fast setup of medical image classification pipelines with state-of-the-art methods via an intuitive, high-level Python API or via an AutoML deployment through Docker/CLI.

## Resources
- Website: [AUCMEDI Website - Home](https://frankkramer-lab.github.io/aucmedi/)
- Git Repository: [GitHub - frankkramer-lab/aucmedi](https://github.com/frankkramer-lab/aucmedi)
- Documentation: [AUCMEDI Wiki - API Reference](https://frankkramer-lab.github.io/aucmedi/reference/)
- Getting Started: [AUCMEDI Website - Getting Started](https://frankkramer-lab.github.io/aucmedi/getstarted/intro/)
- Examples: [AUCMEDI Wiki - Examples](https://frankkramer-lab.github.io/aucmedi/examples/framework/)
- Tutorials: [AUCMEDI Wiki - Tutorials](https://frankkramer-lab.github.io/aucmedi/examples/tutorials/)
- Applications: [AUCMEDI Wiki - Applications](https://frankkramer-lab.github.io/aucmedi/examples/applications/)
- PyPI Package: [PyPI - aucmedi](https://pypi.org/project/aucmedi/)
- Docker Image: [GitHub - ghcr.io/frankkramer-lab/aucmedi](https://github.com/frankkramer-lab/aucmedi/pkgs/container/aucmedi)
- Zenodo Repository: [Zenodo - AUCMEDI](https://zenodo.org/record/6633540)

## How to cite

AUCMEDI is currently unpublished. But coming soon!

In the meantime:  
Please cite our base framework MIScnn as well as the AUCMEDI GitHub repository:

```
Müller, D., Kramer, F. MIScnn: a framework for medical image segmentation with
convolutional neural networks and deep learning. BMC Med Imaging 21, 12 (2021).
https://doi.org/10.1186/s12880-020-00543-7
```

```
Müller, D., Mayer, S., Hartmann, D., Meyer, P., Schneider, P., Soto-Rey, I., & Kramer, F. (2022).
AUCMEDI: a framework for Automated Classification of Medical Images (Version X.Y.Z) [Computer software].
GitHub repository. https://github.com/frankkramer-lab/aucmedi
```

Thank you for citing our work.

### Lead Author

Dominik Müller\
Email: dominik.mueller@informatik.uni-augsburg.de\
IT-Infrastructure for Translational Medical Research\
University Augsburg\
Bavaria, Germany

## License

This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\
See the LICENSE.md file for license rights and limitations.

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Dominik Müller
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