| # 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`. | |