A low-code Python-based open source deep learning library built on top of fastai, MONAI, and TorchIO.

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.

Note: This documentation is also available as interactive notebooks.

pip install fastMONAI

If you want to install an editable version of fastMONAI run:

• git clone https://github.com/MMIV-ML/fastMONAI
• pip install -e 'fastMONAI[dev]'

The best way to get started using fastMONAI is to read the paper and look at the step-by-step tutorial-like notebooks to learn how to train your own models on different tasks (e.g., classification, regression, segmentation). See the docs at https://fastmonai.no for more information.

Notebook	1-Click Notebook
09a_tutorial_classification.ipynb shows how to construct a binary classification model based on MRI data.
09b_tutorial_regression.ipynb shows how to construct a model to predict the age of a subject from MRI scans (“brain age”).
09c_tutorial_binary_segmentation.ipynb shows how to do binary segmentation (extract the left atrium from monomodal cardiac MRI).
09d_tutorial_multiclass_segmentation.ipynb shows how to perform segmentation from multimodal MRI (brain tumor segmentation).

See CONTRIBUTING.md

@article{kaliyugarasan2022fastMONAI,
  title={fastMONAI: a low-code deep learning library for medical image analysis},
  author={Kaliyugarasan, Satheshkumar and Lundervold, Alexander Selvikv{\aa}g},
  year={2022}
}