Most of the examples and tutorials require matplotlib and Jupyter Notebook.

These could be installed by:

python -m pip install -U pip
python -m pip install -U matplotlib
python -m pip install -U notebook

Training and evaluation examples of 3D classification based on DenseNet3D and IXI dataset: The examples are standard PyTorch programs and have both dictionary-based and array-based transformation versions.

Training and evaluation examples of 3D classification based on DenseNet3D and IXI dataset: The examples are PyTorch ignite programs and have both dictionary-based and array-based transformation versions.

This notebook demonstrates the transformations on volumetric images.

This tutorial shows how to accelerate PyTorch medical DL program based on MONAI CacheDataset.

This notebook shows how to easily integrate MONAI features into existing PyTorch programs. It's based on the MedNIST dataset which is very suitable for beginners as a tutorial. The content is also available as a Colab tutorial.

This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.

Illustrate reading NIfTI files and iterating over image patches of the volumes loaded from them.

This notebook is an end-to-end training and evaluation example of 3D segmentation based on MSD Spleen dataset: The example shows the flexibility of MONAI modules in a PyTorch-based program:

• Transforms for dictionary-based training data structure.
• Load NIfTI images with metadata.
• Scale medical image intensity with expected range.
• Crop out a batch of balanced image patch samples based on positive / negative label ratio.
• Cache IO and transforms to accelerate training and validation.
• 3D UNet, Dice loss function, Mean Dice metric for 3D segmentation task.
• Sliding window inference.
• Deterministic training for reproducibility.

This notebook shows how MONAI may be used in conjunction with the PyTorch Lightning framework.

Illustrate reading NIfTI files and test speed of different transforms on different devices.

This notebook demonstrates the image transformations on histology images using the GlaS Contest dataset.

This notebook is an end-to-end training & evaluation example of 3D segmentation based on synthetic dataset. The example is a PyTorch Ignite program and shows several key features of MONAI, especially with medical domain specific transforms and event handlers.

Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset. The examples are standard PyTorch programs and have both dictionary-based and array-based versions.

Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset. The examples are PyTorch Ignite programs and have both dictionary-base and array-based transformations.