MONAI Core is the flagship library of Project MONAI and provides domain-specific capabilities for training AI models for healthcare imaging. These capabilities include medical-specific image transforms, state-of-the-art transformer-based 3D Segmentation algorithms like UNETR, and an AutoML framework named DiNTS.


$ pip install monai

Focuses on Researchers and Data Scientist

MONAI Core is focused on bringing the state-of-the-art to the broader medical community. You can see some of these features below and start using them today through our tutorials.

MedNIST Intro

The Basics

Learn how to use MONAI Transforms, Datasets, and Networks on MedNIST.

Low-Code Segmentation Framework


A comprehensive solution for large-scale 3D medical image segmentation.

Self-Supervised Learning

Utilize Unlabeled Data

Generate pre-trained weights using unlabeled data then use the pre-weights to fine-tune.

Transformer-Based Nets


The foundation of a new class of transformer-based models with hierarchical encoders.

Use what you need

MONAI Core makes it easy to use only the functionality you need. By focusing on extending PyTorch classes, you can integrate most MONAI APIs directly in to your current workflow.

Install MONAI Core

Import MONAI Components and Articles

Get Involved in the Community

Below are ways that you can start using, contributing, and interacting with other members of the MONAI Core community!


Start contributing to the MONAI Core repo by submiting a bug, requesting a feature, or starting a discussion today!


We have an extensive tutorials repo including new notebooks every release to highlight new features so it's easy for you to get started.


Having trouble? Trying to find out where to contribute? Wnat to share your cool project? Join our MONAI Slack and chat with the community.


Ready to get started using MONAI Core? You can find all of our documentation to help get you started or answer any of your questions!