Getting Started AI in Healthcare with MONAI
Project development in AI in healthcare domain can be exhausting without MONAI. Because computer vision and image processing methods in healthcare are quite different from regular methods. Basically, evaluation methods and KPIs are different in medical models from others. Additionally, there are many imaging modalities and every modality has its own data and labelling structure. We need healthcare-specific solutions to standardize methods and reproduce research results.
What is MONAI?
Project MONAI is started by NVIDIA and King’s College London and MONAI is developed by Project MONAI. Its name comes from initials of Medical Open Network for Artificial Intelligence and it is a Python framework for healthcare imaging. It provides healthcare domain-optimized features for developers. It brings PyTorch ecosystem to healthcare-specific applications because it is based on PyTorch. It is published open-source and distributed with Apache 2.0 licence.
It solves many of our problems when we encounter developing AI models in healthcare. Shortly, it brings standardized, user friendly, reproducible and high quality code structure and it covers end-to-end workflow with labeling, training and deployment tools. You can visit MONAI Project web site from here.
Why is MONAI Needed?
Actually, the answer is obvious. We want to solve biomedical problems fast and efficient. Because biomedical applications of AI needs some different methodologies from other areas and they have specific requirements. Medical image’s data structure is different from regular images. So, modalities like MRI, CT, USG have different processing methods and paradigms like voxel spacing, HU. Also medical images have different file format like DICOM, NIfTI unlike regular image formats like PNG, JPG.
Additionally, problems in medical AI can be highly domain specific. We should solve these in efficient and specific way. Lastly, some network architectures are highly successful in biomedical applications. So, we need to collect these architectures in a same bucket.
How to Install MONAI?
MONAI has three components: Label, Core and Deploy. Each component should be installed separately. We can use pip or conda managers to install each component. You can choose one of them according to your development environment.
Install with pip
pip install monai pip install monailabel pip install monai-deploy-app-sdk
Install with conda
conda install -c conda-forge monai conda install -c conda-forge monailabel conda install -c conda-forge monai-deploy-app-sdk
Using MONAI Docker Images
If you are familiar with Docker and containerization you may want to use Docker container of MONAI from Docker Hub. Official Docker image contains all tools that you need to use MONAI. So, working with Docker images is gorgeous and not exhausting when you understand the container mechanics.
Working with Docker containers is beneficial. Because official images are stable environments and you can directly start development with them. If you use these images while development you can also use them in production stage. They significantly reduce your deployment time. Also when new version of MONAI released you just update your container and then your environments and dependencies are updated.
If you want to use MONAI with Docker images you can read my How to Use MONAI Docker Images post.
How to Learn MONAI?
MONAI has an awesome community. All resources are growing and getting better day by day. I believe that someone will help you when you stuck in your project. Now, I want to mention key resources for starting to learn.
MONAI Documentation
My first station is official documentation every time when I start to learn new framework or technology. Because documentations are generally the most frequently updated contents for tools. Also being familiar with documentation will reduce your error rates and error solving times a lot.
MONAI Tutorials Repository
The best place to learn from others’ code is MONAI Tutorials repository. This repository contains dozens of tutorials in Jupyter Notebook format. So, it is really easy to follow and learn best practices.
MONAI Webinars
MONAI is strongly backed by NVIDIA and they consistently organizing events, meetups and webinars. They mention best practices, specific features for healthcare and roadmap all the time. You can access all these resources free on NVIDIA On Demand.
MONAI Discord Community
Discord is an awesome platform for engagement and MONAI has inspiring community. You can join the community from this link and engage with them.
Conclusion
MONAI is an innovating and easy to use machine learning framework, created based on PyTorch. It has specific modules for healthcare and medical imaging. If you are working on AI in healthcare you should definitely give a chance.
