From 1538788fd7663a19d3f2687c101294e3292ceb5f Mon Sep 17 00:00:00 2001 From: Md Arafat Hossain Date: Sun, 25 Oct 2026 00:47:15 +0600 Subject: [PATCH] Revise README for improved clarity and formatting Updated README to enhance clarity and consistency in descriptions, headings, and links. Signed-off-by: Md Arafat Hossain --- README.md | 101 +++++++++++++++++++++++++----------------------------- 1 file changed, 46 insertions(+), 55 deletions(-) diff --git a/README.md b/README.md index 92e4ca7eba8..bddd6a47691 100644 --- a/README.md +++ b/README.md @@ -1,104 +1,95 @@

-project-monai +Project MONAI

-**M**edical **O**pen **N**etwork for **AI** +**MONAI: Medical Open Network 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) -[![auto-commit-msg](https://img.shields.io/badge/dynamic/json?label=citations&query=%24.citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2FDOI%3A10.48550%2FarXiv.2211.02701%3Ffields%3DcitationCount)](https://arxiv.org/abs/2211.02701) [![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai) [![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker&logoColor=white)](https://hub.docker.com/r/projectmonai/monai) [![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai) - -[![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) -[![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) [![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://monai.readthedocs.io/en/latest/) -[![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI) -[![monai Downloads Last Month](https://assets.piptrends.com/get-last-month-downloads-badge/monai.svg 'monai Downloads Last Month by pip Trends')](https://piptrends.com/package/monai) -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: +MONAI is a free and open-source deep learning framework for medical imaging, built on top of [PyTorch](https://pytorch.org/) and listed in the [PyTorch Ecosystem](https://pytorch.org/ecosystem/). The project aims to: -- 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. +- Bring academic, industry and clinical researchers together around one shared foundation; +- Offer modern, complete training pipelines for medical imaging tasks; +- Give researchers a standard, well-optimized way to build and assess deep learning models. -## Features +## Key Capabilities -> _Please see [the technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and [What's New](https://monai.readthedocs.io/en/latest/whatsnew.html) of the milestone releases._ +> For details, see the [technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and the [release notes](https://monai.readthedocs.io/en/latest/whatsnew.html). -- 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. +- Pre-processing that can be adapted to medical images of any dimensionality; +- Modular, portable APIs that slot easily into existing code; +- Networks, loss functions, metrics and other components designed for the medical domain; +- A design that suits users with different levels of experience; +- Data-parallel training across several GPUs and several machines. -## Requirements +## Dependencies -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. +MONAI runs on the Python versions that are still officially supported. It depends directly on NumPy and PyTorch, plus a number of optional packages. -* 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 `pyproject.toml` file for dependency version information. +* Each major release lists its own dependency versions. The `dev` branch is the in-progress version and usually tracks current dependencies, including the fixes needed to keep up with them. +* Supported PyTorch versions are the newest release and the three minor versions before it. If a PyTorch version clashes with another dependency, support for it may wait until the next major release. +* For other dependencies the project mostly follows [SPEC0](https://scientific-python.org/specs/spec-0000), supporting versions for up to about two years. A version with a known vulnerability or defect may be explicitly dropped. +* Exact version requirements are in `pyproject.toml`. -## Installation +## How to Install -To install [the current release](https://pypi.org/project/monai/), you can simply run: +The latest [stable release](https://pypi.org/project/monai/) installs with a single command: ```bash pip install monai ``` -Please refer to [the installation guide](https://monai.readthedocs.io/en/latest/installation.html) for other installation options. - -## Getting Started +Other ways to install are covered in the [installation guide](https://monai.readthedocs.io/en/latest/installation.html). -[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. +## Where to Begin -Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials). +Two interactive notebooks run directly in Colab: the [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). -Technical documentation is available at [monai.readthedocs.io](https://monai.readthedocs.io). +More examples and notebooks live in the [tutorials repository](https://github.com/Project-MONAI/tutorials). Full technical documentation is at [monai.readthedocs.io](https://monai.readthedocs.io). ## Docker -The MONAI Docker image is available from [Dockerhub](https://hub.docker.com/r/projectmonai/monai), -tagged as `latest` for the latest state of `dev` or with a release version. A slimmed down image can also be built -locally using `Dockerfile.slim`, see that file for instructions. +A ready-made image is published on [Docker Hub](https://hub.docker.com/r/projectmonai/monai). The `latest` tag follows the current `dev` branch, and release versions have their own tags. If you want a smaller image, you can build one yourself from `Dockerfile.slim`; the instructions are inside that file. + +To start a container with the newest MONAI and GPU access: -To get started with the latest MONAI, use `docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash`. +```bash +docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash +``` -## Citation +## Citing MONAI -If you have used MONAI in your research, please cite us! The citation can be exported from: . +If MONAI helped with your research, please give credit by citing it. A citation can be exported from . ## 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://monai.readthedocs.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. +The [MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a shared collection where the community publishes strong models. Models use the [MONAI Bundle format](https://monai.readthedocs.io/en/latest/bundle_intro.html), which makes it simple to [start building workflows](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo). ## Contributing -For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md). +To learn how to contribute, read 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). +Follow or talk to the project on Twitter/X ([@ProjectMONAI](https://twitter.com/ProjectMONAI)) or [LinkedIn](https://www.linkedin.com/company/projectmonai), or join the [Slack channel](https://forms.gle/QTxJq3hFictp31UM9). -Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions). +Questions and answers are welcome in the [GitHub Discussions](https://github.com/Project-MONAI/MONAI/discussions) section. -## Links +## Useful Links - Website: -- API documentation (milestone): -- API documentation (latest dev): -- Code: -- Project tracker: -- Issue tracker: +- Documentation (stable): +- Documentation (latest dev): +- Source code: +- Project board: +- Issues: - Wiki: -- Test status: -- PyPI package: +- CI status: +- PyPI: - conda-forge: -- Weekly previews: +- Weekly preview builds: - Docker Hub: