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Releases: Project-MONAI/MONAI

0.2.0

07 Jul 14:16
@wyli wyli
0.2.0
e1bf529
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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
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0.1.0

21 Apr 15:53
@wyli wyli
0.1.0
4a5f3ac
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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
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