Metadata-Version: 2.1
Name: fastMONAI
Version: 0.1.1
Summary: fastMONAI library
Home-page: https://github.com/MMIV-ML/fastMONAI
Author: Satheshkumar Kaliyugarasan
Author-email: skka@hvl.no
License: Apache Software License 2.0
Keywords: deep learning,medical imaging
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastai (==2.7.9)
Requires-Dist: monai (==0.8.1)
Requires-Dist: torchio (==0.18.76)
Requires-Dist: xlrd
Provides-Extra: dev
Requires-Dist: ipywidgets ; extra == 'dev'
Requires-Dist: nbdev ; extra == 'dev'
Requires-Dist: tabulate ; extra == 'dev'

Overview
================

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![](https://github.com/skaliy/skaliy.github.io/blob/master/assets/fastmonai_v1.png?raw=true)

A low-code Python-based open source deep learning library built on top
of [fastai](https://github.com/fastai/fastai),
[MONAI](https://monai.io/), and
[TorchIO](https://torchio.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.

## Installing

### From PyPI

`pip install fastMONAI`

### From Github

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

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

## How to use fastMONAI

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.

## Citing fastMONAI

    @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}
    }
