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
- Git Repository: GitHub - frankkramer-lab/aucmedi
- Documentation: AUCMEDI Wiki - API Reference
- Getting Started: AUCMEDI Website - Getting Started
- Examples: AUCMEDI Wiki - Examples
- Tutorials: AUCMEDI Wiki - Tutorials
- Applications: AUCMEDI Wiki - Applications
- PyPI Package: PyPI - aucmedi
- Docker Image: GitHub - ghcr.io/frankkramer-lab/aucmedi
- Zenodo Repository: Zenodo - AUCMEDI
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AUCMEDI is currently unpublished.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.
1 maintainer
Metadata
1 maintainer
Release files for aucmedi 0.11.0
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