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.
