https://github.com/alexander-jing/monai
AI Toolkit for Healthcare Imaging
Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (18.8%) to scientific vocabulary
Repository
AI Toolkit for Healthcare Imaging
Basic Info
- Host: GitHub
- Owner: Alexander-Jing
- License: apache-2.0
- Default Branch: dev
- Homepage: https://monai.io/
- Size: 45.3 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Medical Open Network for AI
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch 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
The codebase is currently under active development. Please see the technical highlights and What's New of the current milestone release.
- 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, you can simply run:
bash
pip install monai
For other installation methods (using the default GitHub branch, using Docker, etc.), please refer to the installation guide.
Getting Started
MedNIST demo and MONAI for PyTorch Users are available on Colab.
Examples and notebook tutorials are located at Project-MONAI/tutorials.
Technical documentation is available at docs.monai.io.
Contributing
For guidance on making a contribution to MONAI, see the contributing guidelines.
Community
Join the conversation on Twitter @ProjectMONAI or join our Slack channel.
Ask and answer questions over on MONAI's GitHub Discussions tab.
Links
- Website: https://monai.io/
- API documentation: https://docs.monai.io
- 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/
- Weekly previews: https://pypi.org/project/monai-weekly/
- Docker Hub: https://hub.docker.com/r/projectmonai/monai
Owner
- Name: Jing
- Login: Alexander-Jing
- Kind: user
- Location: Beijing
- Company: CASIA
- Repositories: 2
- Profile: https://github.com/Alexander-Jing
UCAS
Citation (CITATION.cff)
# YAML 1.2
# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/)
#
---
title: "MONAI: Medical Open Network for AI"
abstract: "AI Toolkit for Healthcare Imaging"
authors:
- name: "MONAI Consortium"
date-released: 2022-02-16
version: "0.8.1"
identifiers:
- description: "This DOI represents all versions of MONAI, and will always resolve to the latest one."
type: doi
value: "10.5281/zenodo.4323058"
license: "Apache-2.0"
repository-code: "https://github.com/Project-MONAI/MONAI"
url: "https://monai.io"
cff-version: "1.2.0"
message: "If you use this software, please cite it using these metadata."
...