Active Learning Toolkit for Healthcare Imaging
Project description
MONAILabel
MONAILabel is a server-client system that facilitates interactive medical image annotation by using AI. It is an open-source and easy-to-install ecosystem that can run locally on a machine with one or two GPUs. Both server and client work on the same/different machine. However, initial support for multiple users is restricted. It shares the same principles with MONAI.
Development in Progress. We will be actively working on this repository to add more features, fix issues, update docs, readme etc... as we make more progress. Wiki's, LICENSE, Contributions, Code Compliance, CI Tool Integration etc... Otherwise, it shares the same principles with MONAI.
Features
The codebase is currently under active development.
- framework for developing and deploying MONAILabel Apps to train and infer AI models
- compositional & portable APIs for ease of integration in existing workflows
- customizable design for varying user expertise
- 3DSlicer support
Installation
MONAILabel supports following OS with GPU/CUDA enabled.
Ubuntu
# One time setup (to pull monai with nvidia gpus)
docker run -it --rm --gpus all --ipc=host --net=host -v /rapid/xyz:/workspace/ projectmonai/monai:0.5.2
# Install monailabel
pip install git+https://github.com/Project-MONAI/MONAILabel#egg=monailabel
# Download MSD Datasets
monailabel datasets # list sample datasets
monailabel datasets --download --name Task02_Heart --output /workspace/datasets/
# Download Sample Apps
monailabel apps # list sample apps
monailabel apps --download --name deepedit_left_atrium --output /workspace/apps/
# Start Server
monailabel start_server --app /workspace/apps/deepedit_left_atrium --studies /workspace/datasets/Task02_Heart/imagesTr
Windows
Prerequisites
Make sure you have python 3.x version environment with PyTorch + CUDA installed.
- Install python
- Install cuda (Faster mode: install CUDA runtime only)
python -m pip install --upgrade pip setuptools wheelpip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.htmlpython -c "import torch; print(torch.cuda.is_available())"
MONAILabel
pip install git+https://github.com/Project-MONAI/MONAILabel#egg=monailabel
monailabel -h
# Download MSD Datasets
monailabel datasets # List sample datasets
monailabel datasets --download --name Task02_Heart --output C:\Workspace\Datasets
# Download Sample Apps
monailabel apps # List sample apps
monailabel apps --download --name deepedit_left_atrium --output C:\Workspace\Apps
# Start Server
monailabel start_server --app C:\Workspace\Apps\deepedit_left_atrium --studies C:\Workspace\Datasets\Task02_Heart\imagesTr
Once you start the MONAILabel Server, by default it will be up and serving at http://127.0.0.1:8000/. Open the serving URL in browser. It will provide you the list of Rest APIs available.
3D Slicer
Refer 3D Slicer plugin for installing and running MONAILabel plugin in 3D Slicer.
Contributing
For guidance on making a contribution to MONAILabel, see the contributing guidelines.
Community
Join the conversation on Twitter @ProjectMONAI or join our Slack channel.
Ask and answer questions over on MONAILabel's GitHub Discussions tab.
Links
- Website: https://monai.io/
- API documentation: https://docs.monai.io/projects/label
- Code: https://github.com/Project-MONAI/MONAILabel
- Project tracker: https://github.com/Project-MONAI/MONAILabel/projects
- Issue tracker: https://github.com/Project-MONAI/MONAILabel/issues
- Wiki: https://github.com/Project-MONAI/MONAILabel/wiki
- Test status: https://github.com/Project-MONAI/MONAILabel/actions
- PyPI package: https://pypi-hypernode.com/project/monailabel/
- Weekly previews: https://pypi-hypernode.com/project/monailabel-weekly/
- Docker Hub: https://hub.docker.com/r/projectmonai/monailabel
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