MONAI
apps: high level medical domain specific deep learning applications.
auto3dseg: automated machine learning (AutoML) components for volumetric image analysis.
bundle: components to build the portable self-descriptive model bundle.
config: for system configuration and diagnostic output.
csrc: for C++/CUDA extensions.
data: for the datasets, readers/writers, and synthetic data.
engines: engine-derived classes for extending Ignite behaviour.
fl: federated learning components to allow pipeline integration with any federated learning framework.
handlers: defines handlers for implementing functionality at various stages in the training process.
inferers: defines model inference methods.
losses: classes defining loss functions, which follow the pattern of
torch.nn.modules.loss.metrics: defines metric tracking types.
networks: contains network definitions, component definitions, and Pytorch specific utilities.
optimizers: classes defining optimizers, which follow the pattern of
torch.optim.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.
_extensions: C++/CUDA extensions to be loaded in a just-in-time manner using
torch.utils.cpp_extension.load.