Medical Open Network for AI

Getting Started

Install monai

You can quickly get started with the Python Package Index (PyPI) or via several alternative installation approaches

User Guides

MedNIST image classification
Getting started with a GPU powered Colab tutorial

MONAI for PyTorch users
Getting started with an end to end Colab tutorial

Abdominal CT segmentation with 3D UNet
Medical image segmentation tutorial

Histology image transformation demo
2D transformation demo

Volumetric image transformation demo
3D transformation tutorial

Accelerate model training by caching the preprocessors
Demo on the caching dataset

Data-parallel training with multiple-GPUs
Multi-GPU training demo

GPU-based preprocessing in native PyTorch
GPU-based pipeline demo

Integrate 3rd party transforms
Integrate BatchGenerator, TorchIO, Rising and ITK transforms

GAN tutorial with MedNIST dataset
Generate medical images with GAN

Accelerate model training by persistent caching
Demo on the persistent caching dataset

Post transforms tutorial
Post transforms demo for model output

Public Datasets tutorial
Easily use and create public Datasets

Build a segmentation workflow (with PyTorch Ignite)
Segmentation workflow demo

Build a segmentation workflow (with PyTorch Lightning)
Segmentation workflow demo

Developer's guide on MONAI transformations
Concepts of the MONAI transformations

Workflows

Training and evaluation code examples for medical image classification
Brain MRI classification examples

Training and evaluation code examples for medical image classification (with PyTorch Ignite)
Brain MRI classification examples with Ignite

Training and evaluation code examples for medical image segmentation
Volumetric image segmentation examples

Training and evaluation code examples for medical image segmentation (with PyTorch Ignite)
Volumetric image segmentation examples with Ignite

Training and evaluation code examples with MONAI workflows
Image segmentation examples with engine and event-handlers