Project MONAI - Open-Source Frameworks for Accelerating Medical Imaging Research and Clinical Collaboration

Project MONAI Open-Source Frameworks for Accelerating Medical Imaging Research and Clinical Collaboration

Free

Home: https://monai.io

Development

#medical imaging
#Deep Learning
#Open Source
#PyTorch
#annotation
#Segmentation

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What is Project MONAI?

Project MONAI is an open-source initiative providing a set of collaborative frameworks specifically built for accelerating research and clinical applications in the field of medical imaging. Built on top of PyTorch and released under the Apache 2.0 license, it offers a standardized, user-friendly, and reproducible approach to medical imaging and deep learning research and deployment.

MONAI is designed for easy integration with existing efforts and aims at capturing the best practices of AI development for healthcare researchers. It provides user-comprehensible error messages, easy-to-program API interfaces, and high-quality software with robust validation and documentation.

Features

MONAI Label: An intelligent image labeling and learning tool that uses AI assistance to reduce the time and effort of annotating new datasets.
MONAI Core: Provides domain-specific capabilities for training AI models for healthcare imaging, including medical image transforms and transformer-based 3D Segmentation algorithms.
MONAI Deploy: A framework for developing, packaging, testing, deploying, and running medical AI applications in clinical production.

Use Cases

Annotating medical images to create AI annotation models.
Training AI models for healthcare imaging using medical-specific image transforms.
Developing and deploying medical AI applications in clinical production.