Metadata-Version: 2.1
Name: monai-weekly
Version: 1.2.dev2320
Summary: AI Toolkit for Healthcare Imaging
Home-page: https://monai.io/
Author: MONAI Consortium
Author-email: monai.contact@gmail.com
License: Apache License 2.0
Project-URL: Documentation, https://docs.monai.io/
Project-URL: Bug Tracker, https://github.com/Project-MONAI/MONAI/issues
Project-URL: Source Code, https://github.com/Project-MONAI/MONAI
Platform: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Typing :: Typed
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: all
Provides-Extra: nibabel
Provides-Extra: ninja
Provides-Extra: skimage
Provides-Extra: pillow
Provides-Extra: tensorboard
Provides-Extra: gdown
Provides-Extra: ignite
Provides-Extra: torchvision
Provides-Extra: itk
Provides-Extra: tqdm
Provides-Extra: lmdb
Provides-Extra: psutil
Provides-Extra: cucim
Provides-Extra: openslide
Provides-Extra: tifffile
Provides-Extra: imagecodecs
Provides-Extra: pandas
Provides-Extra: einops
Provides-Extra: transformers
Provides-Extra: mlflow
Provides-Extra: matplotlib
Provides-Extra: tensorboardX
Provides-Extra: pyyaml
Provides-Extra: fire
Provides-Extra: jsonschema
Provides-Extra: pynrrd
Provides-Extra: pydicom
Provides-Extra: h5py
Provides-Extra: nni
Provides-Extra: optuna
Provides-Extra: onnx
License-File: LICENSE

<p align="center">
  <img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" width="50%" alt='project-monai'>
</p>

**M**edical **O**pen **N**etwork for **AI**

![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg)
[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
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[![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai)

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MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
Its ambitions are:
- developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
- creating state-of-the-art, end-to-end training workflows for healthcare imaging;
- providing researchers with the optimized and standardized way to create and evaluate deep learning models.


## Features
> _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the milestone releases._

- flexible pre-processing for multi-dimensional medical imaging data;
- compositional & portable APIs for ease of integration in existing workflows;
- domain-specific implementations for networks, losses, evaluation metrics and more;
- customizable design for varying user expertise;
- multi-GPU data parallelism support.


## Installation

To install [the current release](https://pypi.org/project/monai/), you can simply run:

```bash
pip install monai
```

Please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html) for other installation options.

## Getting Started

[MedNIST demo](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) are available on Colab.

Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials).

Technical documentation is available at [docs.monai.io](https://docs.monai.io).

## Citation

If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.

## Model Zoo
[The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community.
Utilizing [the MONAI Bundle format](https://docs.monai.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI.

## Contributing
For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md).

## Community
Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9).

Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions).

## Links
- Website: https://monai.io/
- API documentation (milestone): https://docs.monai.io/
- API documentation (latest dev): https://docs.monai.io/en/latest/
- Code: https://github.com/Project-MONAI/MONAI
- Project tracker: https://github.com/Project-MONAI/MONAI/projects
- Issue tracker: https://github.com/Project-MONAI/MONAI/issues
- Wiki: https://github.com/Project-MONAI/MONAI/wiki
- Test status: https://github.com/Project-MONAI/MONAI/actions
- PyPI package: https://pypi.org/project/monai/
- conda-forge: https://anaconda.org/conda-forge/monai
- Weekly previews: https://pypi.org/project/monai-weekly/
- Docker Hub: https://hub.docker.com/r/projectmonai/monai
