# Overview


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![](https://raw.githubusercontent.com/skaliy/skaliy.github.io/main/assets/fastmonai_v1.png)

![CI](https://github.com/MMIV-ML/fastMONAI/workflows/CI/badge.svg)
[![Docs](https://github.com/MMIV-ML/fastMONAI/actions/workflows/deploy.yaml/badge.svg)](https://fastmonai.no)
[![PyPI](https://img.shields.io/pypi/v/fastMONAI?color=blue&label=PyPI%20version&logo=python&logoColor=white.png)](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 |
|:---|----|
| [11b_tutorial_classification.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/11b_tutorial_classification.ipynb) <br>shows how to construct a binary classification model based on MRI data. | [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/11b_tutorial_classification.ipynb) |
| [11c_tutorial_regression.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/11c_tutorial_regression.ipynb) <br>shows how to construct a model to predict the age of a subject from MRI scans (“brain age”). | [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/11c_tutorial_regression.ipynb) |
| [11d_tutorial_binary_segmentation.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/11d_tutorial_binary_segmentation.ipynb) <br>shows how to do binary segmentation (extract the left atrium from monomodal cardiac MRI). | [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/11d_tutorial_binary_segmentation.ipynb) |
| [11e_tutorial_multiclass_segmentation.ipynb](https://nbviewer.org/github/MMIV-ML/fastMONAI/blob/main/nbs/11e_tutorial_multiclass_segmentation.ipynb) <br>shows how to perform segmentation from multimodal MRI (brain tumor segmentation). | [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/main/nbs/11e_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.}
    }
