Metadata-Version: 2.1
Name: monai
Version: 1.5.2
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 :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
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: >=3.9
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: all
Provides-Extra: clearml
Provides-Extra: cucim
Provides-Extra: einops
Provides-Extra: fire
Provides-Extra: gdown
Provides-Extra: h5py
Provides-Extra: huggingface_hub
Provides-Extra: ignite
Provides-Extra: imagecodecs
Provides-Extra: itk
Provides-Extra: jsonschema
Provides-Extra: lmdb
Provides-Extra: lpips
Provides-Extra: matplotlib
Provides-Extra: mlflow
Provides-Extra: nibabel
Provides-Extra: ninja
Provides-Extra: nni
Provides-Extra: onnx
Provides-Extra: openslide
Provides-Extra: optuna
Provides-Extra: packaging
Provides-Extra: pandas
Provides-Extra: pillow
Provides-Extra: polygraphy
Provides-Extra: psutil
Provides-Extra: pyamg
Provides-Extra: pydicom
Provides-Extra: pynrrd
Provides-Extra: pynvml
Provides-Extra: pyyaml
Provides-Extra: scipy
Provides-Extra: skimage
Provides-Extra: tensorboard
Provides-Extra: tensorboardX
Provides-Extra: tifffile
Provides-Extra: torchio
Provides-Extra: torchvision
Provides-Extra: tqdm
Provides-Extra: transformers
Provides-Extra: zarr
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)
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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 the [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
Its ambitions are as follows:

- 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 multi-node data parallelism support.

## Requirements

MONAI works with the [currently supported versions of Python](https://devguide.python.org/versions), and depends directly on NumPy and PyTorch with many optional dependencies.

* Major releases of MONAI will have dependency versions stated for them. The current state of the `dev` branch in this repository is the unreleased development version of MONAI which typically will support current versions of dependencies and include updates and bug fixes to do so.
* PyTorch support covers [the current version](https://github.com/pytorch/pytorch/releases) plus three previous minor versions. If compatibility issues with a PyTorch version and other dependencies arise, support for a version may be delayed until a major release.
* Our support policy for other dependencies adheres for the most part to [SPEC0](https://scientific-python.org/specs/spec-0000), where dependency versions are supported where possible for up to two years. Discovered vulnerabilities or defects may require certain versions to be explicitly not supported.
* See the `requirements*.txt` files for dependency version information.

## 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/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb) and [MONAI for PyTorch Users](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/developer_guide.ipynb) 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/X [@ProjectMONAI](https://twitter.com/ProjectMONAI), [LinkedIn](https://www.linkedin.com/company/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>


