0.2.0

Added

• Overview document for feature highlights in v0.2.0
• Type hints and static type analysis support
• MONAI/research folder
• monai.engine.workflow APIs for supervised training
• monai.inferers APIs for validation and inference
• 7 new tutorials and examples
• 3 new loss functions
• 4 new event handlers
• 8 new layers, blocks, and networks
• 12 new transforms, including post-processing transforms
• monai.apps.datasets APIs, including MedNISTDataset and DecathlonDataset
• Persistent caching, ZipDataset, and ArrayDataset in monai.data
• Cross-platform CI tests supporting multiple Python versions
• Optional import mechanism
• Experimental features for third-party transforms integration

Changed

For more details please visit the project wiki

• Core modules now require numpy >= 1.17
• Categorized monai.transforms modules into crop and pad, intensity, IO, post-processing, spatial, and utility
• Most transforms are now implemented with PyTorch native APIs
• Code style enforcement and automated formatting workflows now use autopep8 and black
• Base Docker image upgraded to nvcr.io/nvidia/pytorch:20.03-py3 from nvcr.io/nvidia/pytorch:19.10-py3
• Enhanced local testing tools
• Documentation website domain changed to https://docs.monai.io

Removed

• Support of Python < 3.6
• Automatic installation of optional dependencies including pytorch-ignite, nibabel, tensorboard, pillow, scipy, scikit-image

Fixed

• Various issues in type and argument names consistency
• Various issues in docstring and documentation site
• Various issues in unit and integration tests
• Various issues in examples and notebooks