MONAI
MONAI is the domain-specific, open-source Medical AI framework that drives research breakthroughs and accelerates AI into clinical impact.
A PyTorch-based framework for building, training, and deploying deep-learning models for healthcare imaging.
About the Project
Why use this?
MONAI is a domain-specific medical-imaging AI framework built on PyTorch. It provides optimized data transforms, network architectures, loss functions, metrics, and training utilities tailored to 2D and 3D medical imaging workloads.
Bioengineers and AI researchers choose MONAI when they need to develop medical-image segmentation, classification, detection, and self-supervised learning models. Its integrations with 3D Slicer, OHIF, QuPath, Digital Slide Archive, and CVAT help connect model development with visualization, annotation, and clinical-research workflows. [313][317][324]
Key Features
- PyTorch-based medical-imaging AI framework
- Domain-specific transforms, networks, losses, and metrics
- 2D and 3D medical-image deep-learning workflows
- MONAI Label integration with 3D Slicer, OHIF, QuPath, and other viewers
- Strong ecosystem for research-to-clinical AI development
- Reference / Repo
- https://github.com/Project-MONAI/MONAI
- Tool / Hosted
- https://monai.io/
- Replaces / Alternative to
- Proprietary medical-AI development platforms
- Free layer
- open-source