bioMONAI is a low-code Python-based platform for developing and deploying deep learning models in biomedical imaging built on top of the MONAI framework, fastai, and TorchIO. This project aims to facilitate interoperability, reproducibility, and community collaboration in biomedical research.
For more information, bioMONAI documentation can be found here.
To install the bioMONAI environment, follow these steps:
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Clone the repository:
git clone https://github.com/bmandracchia/biomonai.git cd biomonai -
Create a new Conda environment (recommended):
conda create -n biomonai python=3.7 conda activate biomonai
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Install dependencies:
pip install -r requirements.txt
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Install MONAI and other necessary packages:
pip install bioMONAI
To get started with bioMONAI, the best way is to try out our tutorials, which will walk you through model training for various tasks like classification, regression, and segmentation.
| Notebook | Open in Colab |
|---|---|
| Tutorial classification (to be updated.) |
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| Tutorial regression (to be updated.) |
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To use bioMONAI for your own projects, follow these steps:
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Create a new Jupyter notebook or open an existing one.
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Import necessary modules:
import bioMONAI
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Start coding! You can now leverage MONAI’s capabilities alongside the interactive features of Jupyter notebooks.
We welcome contributions from the community! To contribute to BioMONAI nbs, follow these steps:
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Fork the repository on GitHub.
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Clone your fork:
git clone https://github.com/your_username/biomonai.git cd biomonai -
Create a new branch for your changes:
git checkout -b feature/new-feature
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Make your changes and commit them:
git add . git commit -m "Add new feature: <feature description>"
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Push to your fork and create a pull request on GitHub.
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Wait for the review, and merge if everything looks good!
bioMONAI is released under the Apache 2.0 license. See LICENSE for more details.
If you have any questions or need further assistance, please open an issue on GitHub or contact us directly at:
- Project Lead: Biagio Mandracchia
- Contributors: Juan Pita-López, Rosa-María Menchón-Lara