The Leading Open Platform for Medical Data Labeling with AI
MONAI Label is an intelligent open source image labeling and learning tool that enables users to create annotated datasets and build AI annotation models for clinical evaluation. MONAI Label enables application developers to build labeling apps in a serverless way, where custom labeling apps are exposed as a service through the MONAI Label Server.
Sample Apps in MONAILabel
Example developed labeling apps for use. Learn more apps and MONAILabel use cases here.
This app has example models to do both interactive and automated segmentation over radiology (3D) images. Including auto segmentation with latest deep learning models (e.g., UNet, UNETR) for multiple abdominal organs. Interactive tools includes DeepEdit and Deepgrow for actively improve trained models and deployment.
This app has example models to do both interactive and automated segmentation over pathology (WSI) images. Including nuclei multi-label segmentation for Neoplastic cells, Inflammatory, Connective/Soft tissue cells, Dead Cells, and Epithelial. The app provides interactive tools includes DeepEdits for interactive nuclei segmentation.
The Bundle app enables users with customized models for inference, training or pre and post processing any target anatomies. The specification for MONAILabel integration of the Bundle app links archived Model-Zoo for customized labeling (e.g., the third-party transformer model for labeling renal cortex, medulla, and pelvicalyceal system. Interactive tools such as DeepEdits).
- Endoscopy App
The Endoscopy app enables users to use interactive, automated segmentation and classification models over 2D images for endoscopy usecase. Combined with CVAT, it will demonstrate the fully automated Active Learning workflow to train + fine-tune a model.
Deploy Labeling and Medical AI Faster
The end-to-end ecosystem from research stage to easy model deployment. Combining clinical imaging data visualizations, curations with the model inference. After labeling automatically, the visualization tools provide flexibility for label correction. The active learning modules can learn the new labels online to fine tune the current AI models. The fine-tuned model can be improved and used for next batch of labeling task.
Features and Highlights
MONAI Label reduces the time and effort of annotating new datasets and enables the adaptation of AI to the task at hand by continuously learning from user interactions and data. MONAI Label allows researchers and developers to make continuous improvements to their apps by allowing them to interact with their apps at the user would. End-users (clinicians, technologists, and annotators in general) benefit from AI continuously learning and becoming better at understanding what the end-user is trying to annotate.
MONAI Label aims to fill the gap between developers creating new annotation applications, and the end users which want to benefit from these innovations.
Table of Contents¶
For guidance on making a contribution to MONAI, see the contributing guidelines.
API documentation: https://docs.monai.io/projects/label
Project tracker: https://github.com/Project-MONAI/MONAILabel/projects
Issue tracker: https://github.com/Project-MONAI/MONAILabel/issues
Test status: https://github.com/Project-MONAI/MONAILabel/actions
PyPI package: https://pypi.org/project/monailabel/
Weekly previews: https://pypi.org/project/monailabel-weekly/