# Overview
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
[](https://fastmonai.no)
[](https://pypi.org/project/fastMONAI)
A low-code Python-based open source deep learning library built on top
of [fastai](https://github.com/fastai/fastai),
[MONAI](https://monai.io/), [TorchIO](https://torchio.readthedocs.io/),
and [Imagedata](https://imagedata.readthedocs.io/).
fastMONAI simplifies the use of state-of-the-art deep learning
techniques in 3D medical image analysis for solving classification,
regression, and segmentation tasks. fastMONAI provides the users with
functionalities to step through data loading, preprocessing, training,
and result interpretations.
<b>Note:</b> This documentation is also available as interactive
notebooks.
## Requirements
- **Python:** 3.10, 3.11, or 3.12 (Python 3.11 recommended)
- **GPU:** CUDA-compatible GPU recommended for training (CPU supported
for inference)
# Installation
## Environment setup (recommended)
We recommend using a conda environment to avoid dependency conflicts:
`conda create -n fastmonai python=3.11`
`conda activate fastmonai`
## Quick Install [(PyPI)](https://pypi.org/project/fastMONAI/)
`pip install fastMONAI`
## Development install [(GitHub)](https://github.com/MMIV-ML/fastMONAI)
If you want to install an editable version of fastMONAI for development:
git clone https://github.com/MMIV-ML/fastMONAI
cd fastMONAI
# Create development environment
conda create -n fastmonai-dev python=3.11
conda activate fastmonai-dev
# Install in development mode
pip install -e '.[dev]'
# Getting started
The best way to get started using fastMONAI is to read our
[paper](https://www.sciencedirect.com/science/article/pii/S2665963823001203)
and dive into our beginner-friendly [video
tutorial](https://fastmonai.no/tutorial_beginner_video). For a deeper
understanding and hands-on experience, our comprehensive instructional
notebooks will walk you through model training for various tasks like
classification, regression, and segmentation. See the docs at
https://fastmonai.no for more information.
| Notebook | 1-Click Notebook |
|:---|----|
| [10a_tutorial_classification.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10a_tutorial_classification.ipynb) <br>shows how to construct a binary classification model based on MRI data. | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10a_tutorial_classification.ipynb) |
| [10b_tutorial_regression.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10b_tutorial_regression.ipynb) <br>shows how to construct a model to predict the age of a subject from MRI scans (“brain age”). | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10b_tutorial_regression.ipynb) |
| [10c_tutorial_binary_segmentation.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10c_tutorial_binary_segmentation.ipynb) <br>shows how to do binary segmentation (extract the left atrium from monomodal cardiac MRI). | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10c_tutorial_binary_segmentation.ipynb) |
| [10d_tutorial_multiclass_segmentation.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10d_tutorial_multiclass_segmentation.ipynb) <br>shows how to perform segmentation from multimodal MRI (brain tumor segmentation). | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10d_tutorial_multiclass_segmentation.ipynb) |
# How to contribute
We welcome contributions! See
[CONTRIBUTING.md](https://github.com/MMIV-ML/fastMONAI/blob/main/CONTRIBUTING.md)
# Citing fastMONAI
If you are using fastMONAI in your research, please use the following
citation:
@article{KALIYUGARASAN2023100583,
title = {fastMONAI: A low-code deep learning library for medical image analysis},
journal = {Software Impacts},
pages = {100583},
year = {2023},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2023.100583},
url = {https://www.sciencedirect.com/science/article/pii/S2665963823001203},
author = {Satheshkumar Kaliyugarasan and Alexander S. Lundervold},
keywords = {Deep learning, Medical imaging, Radiology},
abstract = {We introduce fastMONAI, an open-source Python-based deep learning library for 3D medical imaging. Drawing upon the strengths of fastai, MONAI, and TorchIO, fastMONAI simplifies the use of advanced techniques for tasks like classification, regression, and segmentation. The library's design addresses domain-specific demands while promoting best practices, facilitating efficient model development. It offers newcomers an easier entry into the field while keeping the option to make advanced, lower-level customizations if needed. This paper describes the library's design, impact, limitations, and plans for future work.}
}
Raw data
{
"_id": null,
"home_page": "https://github.com/MMIV-ML/fastMONAI",
"name": "fastMONAI",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "deep learning, medical imaging",
"author": "Satheshkumar Kaliyugarasan",
"author_email": "skaliyugarasan@hotmail.com",
"download_url": "https://files.pythonhosted.org/packages/3c/a0/0233d4aabc1ef16dddde83f9221f9cc8922d4fb6a9482b4cb8adec18e4ac/fastmonai-0.5.2.tar.gz",
"platform": null,
"description": "# Overview\n\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n\n\n\n[](https://fastmonai.no)\n[](https://pypi.org/project/fastMONAI)\n\nA low-code Python-based open source deep learning library built on top\nof [fastai](https://github.com/fastai/fastai),\n[MONAI](https://monai.io/), [TorchIO](https://torchio.readthedocs.io/),\nand [Imagedata](https://imagedata.readthedocs.io/).\n\nfastMONAI simplifies the use of state-of-the-art deep learning\ntechniques in 3D medical image analysis for solving classification,\nregression, and segmentation tasks. fastMONAI provides the users with\nfunctionalities to step through data loading, preprocessing, training,\nand result interpretations.\n\n<b>Note:</b> This documentation is also available as interactive\nnotebooks.\n\n## Requirements\n\n- **Python:** 3.10, 3.11, or 3.12 (Python 3.11 recommended)\n- **GPU:** CUDA-compatible GPU recommended for training (CPU supported\n for inference)\n\n# Installation\n\n## Environment setup (recommended)\n\nWe recommend using a conda environment to avoid dependency conflicts:\n\n`conda create -n fastmonai python=3.11`\n\n`conda activate fastmonai`\n\n## Quick Install [(PyPI)](https://pypi.org/project/fastMONAI/)\n\n`pip install fastMONAI`\n\n## Development install [(GitHub)](https://github.com/MMIV-ML/fastMONAI)\n\nIf you want to install an editable version of fastMONAI for development:\n\n git clone https://github.com/MMIV-ML/fastMONAI\n cd fastMONAI\n\n # Create development environment\n conda create -n fastmonai-dev python=3.11\n conda activate fastmonai-dev\n\n # Install in development mode\n pip install -e '.[dev]'\n\n# Getting started\n\nThe best way to get started using fastMONAI is to read our\n[paper](https://www.sciencedirect.com/science/article/pii/S2665963823001203)\nand dive into our beginner-friendly [video\ntutorial](https://fastmonai.no/tutorial_beginner_video). For a deeper\nunderstanding and hands-on experience, our comprehensive instructional\nnotebooks will walk you through model training for various tasks like\nclassification, regression, and segmentation. See the docs at\nhttps://fastmonai.no for more information.\n\n| Notebook | 1-Click Notebook |\n|:---|----|\n| [10a_tutorial_classification.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10a_tutorial_classification.ipynb) <br>shows how to construct a binary classification model based on MRI data. | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10a_tutorial_classification.ipynb) |\n| [10b_tutorial_regression.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10b_tutorial_regression.ipynb) <br>shows how to construct a model to predict the age of a subject from MRI scans (\u201cbrain age\u201d). | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10b_tutorial_regression.ipynb) |\n| [10c_tutorial_binary_segmentation.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10c_tutorial_binary_segmentation.ipynb) <br>shows how to do binary segmentation (extract the left atrium from monomodal cardiac MRI). | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10c_tutorial_binary_segmentation.ipynb) |\n| [10d_tutorial_multiclass_segmentation.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/10d_tutorial_multiclass_segmentation.ipynb) <br>shows how to perform segmentation from multimodal MRI (brain tumor segmentation). | [](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/10d_tutorial_multiclass_segmentation.ipynb) |\n\n# How to contribute\n\nWe welcome contributions! See\n[CONTRIBUTING.md](https://github.com/MMIV-ML/fastMONAI/blob/main/CONTRIBUTING.md)\n\n# Citing fastMONAI\n\nIf you are using fastMONAI in your research, please use the following\ncitation:\n\n @article{KALIYUGARASAN2023100583,\n title = {fastMONAI: A low-code deep learning library for medical image analysis},\n journal = {Software Impacts},\n pages = {100583},\n year = {2023},\n issn = {2665-9638},\n doi = {https://doi.org/10.1016/j.simpa.2023.100583},\n url = {https://www.sciencedirect.com/science/article/pii/S2665963823001203},\n author = {Satheshkumar Kaliyugarasan and Alexander S. Lundervold},\n keywords = {Deep learning, Medical imaging, Radiology},\n abstract = {We introduce fastMONAI, an open-source Python-based deep learning library for 3D medical imaging. Drawing upon the strengths of fastai, MONAI, and TorchIO, fastMONAI simplifies the use of advanced techniques for tasks like classification, regression, and segmentation. The library's design addresses domain-specific demands while promoting best practices, facilitating efficient model development. It offers newcomers an easier entry into the field while keeping the option to make advanced, lower-level customizations if needed. This paper describes the library's design, impact, limitations, and plans for future work.}\n }\n",
"bugtrack_url": null,
"license": "Apache Software License 2.0",
"summary": "fastMONAI library",
"version": "0.5.2",
"project_urls": {
"Homepage": "https://github.com/MMIV-ML/fastMONAI"
},
"split_keywords": [
"deep learning",
" medical imaging"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "ea684433f4251baea982ce8ea093a998ee27c0b63ff8b5724301d05be7311a08",
"md5": "cc6e4c8381ad47f3c8d5da550e509bab",
"sha256": "73416d73d290fa7df7755b69e0cc24463236185b44860dc40eddb77de257bc7e"
},
"downloads": -1,
"filename": "fastmonai-0.5.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "cc6e4c8381ad47f3c8d5da550e509bab",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 39235,
"upload_time": "2025-09-02T13:46:34",
"upload_time_iso_8601": "2025-09-02T13:46:34.140454Z",
"url": "https://files.pythonhosted.org/packages/ea/68/4433f4251baea982ce8ea093a998ee27c0b63ff8b5724301d05be7311a08/fastmonai-0.5.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "3ca00233d4aabc1ef16dddde83f9221f9cc8922d4fb6a9482b4cb8adec18e4ac",
"md5": "137da284febf999887efca6528c365c4",
"sha256": "a66b315e76dfb6912451d059ed86e1878b1e6fe469e08b25b37c1bb8e74bc48e"
},
"downloads": -1,
"filename": "fastmonai-0.5.2.tar.gz",
"has_sig": false,
"md5_digest": "137da284febf999887efca6528c365c4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 39118,
"upload_time": "2025-09-02T13:46:35",
"upload_time_iso_8601": "2025-09-02T13:46:35.813659Z",
"url": "https://files.pythonhosted.org/packages/3c/a0/0233d4aabc1ef16dddde83f9221f9cc8922d4fb6a9482b4cb8adec18e4ac/fastmonai-0.5.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-09-02 13:46:35",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "MMIV-ML",
"github_project": "fastMONAI",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "fastmonai"
}
JSON
