MONAI

apps: high level medical domain specific deep learning applications.
config: for system configuration and diagnostic output.
data: for the datasets, readers/writers, and synthetic data
engines: engine-derived classes for extending Ignite behaviour.
handlers: defines handlers for implementing functionality at various stages in the training process.
inferers: defines model inference methods.
losses: classes defining loss functions.
metrics: defines metric tracking types.
networks: contains network definitions, component definitions, and Pytorch specific utilities.
transforms: defines data transforms for preprocessing and postprocessing.
utils: generic utilities intended to be implemented in pure Python or using Numpy, and not with Pytorch, such as namespace aliasing, auto module loading.
visualize: utilities for data visualization.