Project-MONAI/tutorials

MONAI Tutorials

What it solves

This repository provides a comprehensive collection of practical examples and tutorials for using MONAI, an open-source framework for deep learning in healthcare imaging. It helps users learn how to build, train, and deploy medical AI models for tasks like segmentation, classification, registration, and pathology analysis.

How it works

The repository contains a series of Jupyter Notebooks and Python scripts that demonstrate how to integrate MONAI components into standard PyTorch workflows. Users can run these tutorials locally or via Google Colab. The tutorials cover the entire lifecycle of a medical AI project, from data loading and preprocessing to model training, experiment tracking, and deployment.

Who it’s for

This project is for researchers, data scientists, and developers working in medical imaging who want to implement deep learning solutions using MONAI. It is suitable for both beginners looking for simple examples and experienced practitioners needing advanced workflows for distributed training or deployment.

Highlights

  • Extensive library of tutorials covering 2D and 3D medical imaging tasks including segmentation, classification, and registration.
  • Integration examples with popular frameworks like PyTorch Lightning, BentoML, Ray, and MLFlow.
  • Specialized modules for digital pathology, federated learning, and computer-assisted intervention.
  • Performance optimization guides for accelerating training and inference using techniques like Automatic Mixed Precision (AMP) and TensorRT.
  • Ready-to-use examples for experiment management and deployment to production environments.

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