💡 If you want to know more about MONAI Deploy WG vision, overall structure, and guidelines, please read https://github.com/Project-MONAI/monai-deploy first.

MONAI Deploy App SDK

MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.

Features

• Build medical imaging inference applications using a flexible, extensible & usable Pythonic API
• Easy management of inference applications via programmable Directed Acyclic Graphs (DAGs)
• Built-in operators to load DICOM data to be ingested in an inference app
• Out-of-the-box support for in-proc PyTorch based inference
• Easy incorporation of MONAI based pre and post transformations in the inference application
• Package inference application with a single command into a portable MONAI Application Package
• Locally run and debug your inference application using App Runner

User Guide

User guide is available at docs.monai.io.

Installation

To install the current release, you can simply run:

pip install monai-deploy-app-sdk  # '--pre' to install a pre-release version.

Getting Started

Getting started guide is available at here.

pip install monai-deploy-app-sdk  # '--pre' to install a pre-release version.

# Clone monai-deploy-app-sdk repository for accessing examples.
git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git
cd monai-deploy-app-sdk

# Install necessary dependencies for simple_imaging_app
pip install scikit-image

# Execute the app locally
python examples/apps/simple_imaging_app/app.py -i examples/apps/simple_imaging_app/brain_mr_input.jpg -o output

# Package app (creating MAP Docker image), using `-l DEBUG` option to see progress.
monai-deploy package examples/apps/simple_imaging_app -t simple_app:latest -l DEBUG

# Run the app with docker image and an input file locally
## Copy a test input file to 'input' folder
mkdir -p input && rm -rf input/*
cp examples/apps/simple_imaging_app/brain_mr_input.jpg input/
## Launch the app
monai-deploy run simple_app:latest input output

Tutorials

1) Creating a simple image processing app

2) Creating MedNIST Classifier app

YouTube Video:

• MedNIST Classification Example

3) Creating a Segmentation app

YouTube Video:

• Spleen Organ Segmentation - Jupyter Notebook Tutorial
• Spleen Organ Segmentation - Deep Dive

Examples

https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples/apps has example apps that you can see.

• ai_spleen_seg_app
• ai_unetr_seg_app
• dicom_series_to_image_app
• mednist_classifier_monaideploy
• simple_imaging_app

Contributing

For guidance on making a contribution to MONAI Deploy App SDK, see the contributing guidelines.

Community

To participate in the MONAI Deploy WG, please review https://github.com/Project-MONAI/MONAI/wiki/Deploy-Working-Group.

Join the conversation on Twitter @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAI Deploy App SDK's GitHub Discussions tab.

Links

• Website: https://monai.io
• API documentation: https://docs.monai.io/projects/monai-deploy-app-sdk
• Code: https://github.com/Project-MONAI/monai-deploy-app-sdk
• Project tracker: https://github.com/Project-MONAI/monai-deploy-app-sdk/projects
• Issue tracker: https://github.com/Project-MONAI/monai-deploy-app-sdk/issues
• Wiki: https://github.com/Project-MONAI/monai-deploy-app-sdk/wiki
• Test status: https://github.com/Project-MONAI/monai-deploy-app-sdk/actions
• PyPI package: https://pypi.org/project/monai-deploy-app-sdk