# project-monai/monai

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7,869 stars · 1,429 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/Project-MONAI/MONAI
- Homepage: https://project-monai.github.io/
- awesome-repositories: https://awesome-repositories.com/repository/project-monai-monai.md

## Topics

`deep-learning` `healthcare-imaging` `medical-image-computing` `medical-image-processing` `monai` `python3` `pytorch`

## Description

MONAI is a PyTorch-based deep learning framework and library specifically designed for healthcare imaging. It provides a suite of domain-specific neural network architectures, specialized loss functions, and preprocessing pipelines tailored for analyzing multi-dimensional medical data.

The project distinguishes itself through a decentralized federated learning system that allows models to learn from datasets across multiple institutions without exchanging raw patient images. It also features AI-assisted medical image annotation tools and a standardized model bundling system to ensure consistent inference and reproducibility across clinical workstations and cloud environments.

The framework covers the full medical AI lifecycle, including data engineering via spatial resampling and normalization, distributed training across multi-GPU nodes, and model evaluation using specialized imaging metrics and result visualization.

The library is implemented in Python.

## Tags

### Artificial Intelligence & ML

- [Medical Imaging AI Development](https://awesome-repositories.com/f/artificial-intelligence-ml/medical-imaging-ai-development.md) — Offers a comprehensive framework for building and training deep learning models specifically for processing multi-dimensional healthcare imaging data.
- [Model Bundle Specifications](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/model-hubs-and-pre-made-models/model-management-utilities/remote-model-hubs/model-definition-standards/model-bundle-specifications.md) — Packages neural network weights with specific preprocessing transforms and metadata to ensure consistent cross-environment deployment.
- [Federated Learning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/federated-learning-frameworks.md) — Implements a decentralized federated learning system allowing models to learn from institutional data without exchanging raw patient images.
- [Federated Learnings](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/distributed-and-scaling-strategies/distributed-learning/federated-learnings.md) — Implements a decentralized system for training models across remote institutional data sources while keeping raw medical images local.
- [Medical Deep Learning Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/medical-deep-learning-architectures.md) — Provides neural network architectures and specialized loss functions tailored specifically for healthcare imaging and diagnostic tasks. ([source](https://monai.readthedocs.io/en/latest/))
- [Medical Deep Learning Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/medical-deep-learning-libraries.md) — Provides a suite of domain-specific neural network architectures and specialized loss functions designed for analyzing multi-dimensional medical data.
- [Medical Imaging Preprocessing Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/medical-imaging-preprocessing-tools.md) — Prepares multi-dimensional healthcare images through specialized transformations and pipelines to make them ready for model training.
- [Model Packaging](https://awesome-repositories.com/f/artificial-intelligence-ml/model-packaging.md) — Packages models with metadata and documentation in a consistent format to ensure reproducibility across clinical applications. ([source](https://monai.readthedocs.io/en/latest/bundle_intro.html))
- [Neural Network Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-implementations.md) — Implements domain-specific neural network architectures and loss functions to evaluate complex medical imaging tasks. ([source](https://monai.readthedocs.io/))
- [Preprocessing Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/preprocessing-pipelines.md) — Provides flexible and customizable preprocessing pipelines to transform multi-dimensional healthcare data for deep learning models. ([source](https://cdn.jsdelivr.net/gh/project-monai/monai@dev/README.md))
- [Medical Imaging Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-training-frameworks/medical-imaging-training-frameworks.md) — Offers a specialized PyTorch-based framework providing 3D architectures, transforms, and evaluation metrics for healthcare imaging.
- [Tensor Operation Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-operation-implementations.md) — Leverages PyTorch tensor operations and dynamic computation graphs to process high-dimensional medical imaging data.
- [AI-Assisted Labeling](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-assistants/ai-assisted-labeling.md) — Provides AI-assisted labeling of image volumes using active learning and suggestions to support collaborative annotation. ([source](https://project-monai.github.io/))
- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training.md) — Scales deep learning workloads across multiple GPUs and nodes using data parallelism to handle large-scale imaging datasets. ([source](https://monai.readthedocs.io/))
- [Data-Parallel Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/data-parallel-training.md) — Distributes large imaging datasets across multiple GPU nodes using synchronized gradient updates to accelerate training.
- [Distributed Training Scaling Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-scaling-utilities.md) — Distributes deep learning workloads across multiple hardware nodes to reduce the total time required for training. ([source](https://monai.readthedocs.io/en/latest/))
- [Medical Segmentation Loss Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/neural-network-components/loss-functions/perceptual-loss/medical-segmentation-loss-functions.md) — Implements specialized mathematical objectives like Dice and Focal loss to handle extreme class imbalance in medical segmentation.
- [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Increases computation speed by using reduced precision formats on compatible hardware without altering model architecture. ([source](https://monai.readthedocs.io/en/latest/precision_accelerating.html))
- [Model Deployment Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/deployment-pipelines-and-endpoints/model-deployment-pipelines.md) — Packages healthcare models into containers with data support for execution across clinical workstations or cloud clusters. ([source](https://project-monai.github.io/))
- [Model Evaluation Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-evaluation-metrics.md) — Calculates specialized imaging metrics to measure the accuracy of diagnostic models against ground truth data. ([source](https://monai.readthedocs.io/en/latest/api.html))
- [Medical Image Annotation](https://awesome-repositories.com/f/artificial-intelligence-ml/medical-image-annotation.md) — Provides AI-assisted tools and collaborative workflows for labeling 3D medical image volumes to create ground truth datasets.
- [Multi-GPU Training Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-gpu-training-utilities.md) — Spreads training tasks across multiple GPUs and nodes to process large-scale medical datasets more efficiently. ([source](https://cdn.jsdelivr.net/gh/project-monai/monai@dev/README.md))
- [Training Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/training-orchestrators.md) — Manages the training loop through engines and event handlers to automate the model optimization process. ([source](https://monai.readthedocs.io/en/latest/api.html))

### Data & Databases

- [Medical Dataset Formatting](https://awesome-repositories.com/f/data-databases/medical-dataset-formatting.md) — Transforms raw medical images and labels into standardized formats suitable for supervised learning, training, and evaluation. ([source](https://monai.readthedocs.io/en/latest/api.html))
- [Medical Image Normalization](https://awesome-repositories.com/f/data-databases/medical-image-normalization.md) — Provides comprehensive tools for transforming, resampling, and normalizing healthcare image volumes for deep learning input.

### DevOps & Infrastructure

- [Package Distribution Workflows](https://awesome-repositories.com/f/devops-infrastructure/dependency-management/package-distribution-workflows.md) — Bundles models and configurations into a standardized format to simplify the distribution of imaging workflows. ([source](https://monai.readthedocs.io/en/latest/_sources/index.rst.txt))
- [Clinical Model Bundling](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-inference-deployment/clinical-model-bundling.md) — Implements a standardized model bundling system to ensure consistent inference and reproducibility across clinical workstations and cloud environments.
- [Model Inference Deployment](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-inference-deployment.md) — Packages models with required configurations and transforms to ensure consistent inference results across various environments. ([source](https://monai.readthedocs.io/en/latest/api.html))

### Part of an Awesome List

- [Medical Image Segmentation](https://awesome-repositories.com/f/awesome-lists/ai/medical-image-segmentation.md) — Enables the creation of healthcare deep learning workflows using specialized 3D architectures and pre-trained models. ([source](https://cdn.jsdelivr.net/gh/project-monai/monai@dev/README.md))
- [Machine Learning and Analytics](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning-and-analytics.md) — AI toolkit specifically for medical imaging.

### Graphics & Multimedia

- [Medical Prediction Visualizers](https://awesome-repositories.com/f/graphics-multimedia/medical-prediction-visualizers.md) — Generates visual representations of medical images and model predictions for qualitative analysis of diagnostic results. ([source](https://monai.readthedocs.io/en/latest/api.html))

### Software Engineering & Architecture

- [Training Loop Orchestrations](https://awesome-repositories.com/f/software-engineering-architecture/architectural-design-patterns/event-driven-orchestrations/training-loop-orchestrations.md) — Uses a centralized engine and event handlers to automate the training loop and optimization process.
- [Compositional Transformation Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/compositional-transformation-pipelines.md) — Provides architectural patterns for chaining sequential image augmentations and preprocessing operations into a single executable pipeline.

### Testing & Quality Assurance

- [Model Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/model-evaluation.md) — Provides specialized imaging metrics and visualization tools to measure diagnostic model accuracy against clinical ground truth.