0.3.0

Added

• Overview document for feature highlights in v0.3.0
• Automatic mixed precision support
• Multi-node, multi-GPU data parallel model training support
• 3 new evaluation metric functions
• 11 new network layers and blocks
• 6 new network architectures
• 14 new transforms, including an I/O adaptor
• Cross validation module for DecathlonDataset
• Smart Cache module in dataset
• monai.optimizers module
• monai.csrc module
• Experimental feature of ImageReader using ITK, Nibabel, Numpy, Pillow (PIL Fork)
• Experimental feature of differentiable image resampling in C++/CUDA
• Ensemble evaluator module
• GAN trainer module
• Initial cross-platform CI environment for C++/CUDA code
• Code style enforcement now includes isort and clang-format
• Progress bar with tqdm

Changed

• Now fully compatible with PyTorch 1.6
• Base Docker image upgraded to nvcr.io/nvidia/pytorch:20.08-py3 from nvcr.io/nvidia/pytorch:20.03-py3
• Code contributions now require signing off on the Developer Certificate of Origin (DCO)
• Major work in type hinting finished
• Remote datasets migrated to Open Data on AWS
• Optionally depend on PyTorch-Ignite v0.4.2 instead of v0.3.0
• Optionally depend on torchvision, ITK
• Enhanced CI tests with 8 new testing environments

Removed

• MONAI/examples folder (relocated into Project-MONAI/tutorials)
• MONAI/research folder (relocated to Project-MONAI/research-contributions)

Fixed

• dense_patch_slices incorrect indexing
• Data type issue in GeneralizedWassersteinDiceLoss
• ZipDataset return value inconsistencies
• sliding_window_inference indexing and device issues
• importing monai modules may cause namespace pollution
• Random data splits issue in DecathlonDataset
• Issue of randomising a Compose transform
• Various issues in function type hints
• Typos in docstring and documentation
• PersistentDataset issue with existing file folder
• Filename issue in the output writers

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

0.1.0

Added

• Public alpha source code under the Apache 2.0 license (highlights)
• Various tutorials and examples
  • Medical image classification and segmentation workflows
  • Spacing/orientation-aware preprocessing with CPU/GPU and caching
  • Flexible workflows with external engines including PyTorch Ignite and Lightning
• Various GitHub Actions
  • CI/CD pipelines via self-hosted runners
  • Documentation publishing via readthedocs.org
  • PyPI package publishing
• Contributing guidelines
• A project logo and badges