Added

• Overview document for feature highlights in v0.5.0
• Invertible spatial transforms
  • InvertibleTransform base APIs
  • Batch inverse and decollating APIs
  • Inverse of Compose
  • Batch inverse event handling
  • Test-time augmentation as an application
• Initial support of learning-based image registration:
  • Bending energy, LNCC, and global mutual information loss
  • Fully convolutional architectures
  • Dense displacement field, dense velocity field computation
  • Warping with high-order interpolation with C++/CUDA implementations
• Deepgrow modules for interactive segmentation:
  • Workflows with simulations of clicks
  • Distance-based transforms for guidance signals
• Digital pathology support:
  • Efficient whole slide imaging IO and sampling with Nvidia cuCIM and SmartCache
  • FROC measurements for lesion
  • Probabilistic post-processing for lesion detection
  • TorchVision classification model adaptor for fully convolutional analysis
• 12 new transforms, grid patch dataset, ThreadDataLoader, EfficientNets B0-B7
• 4 iteration events for the engine for finer control of workflows
• New C++/CUDA extensions:
  • Conditional random field
  • Fast bilateral filtering using the permutohedral lattice
• Metrics summary reporting and saving APIs
• DiceCELoss, DiceFocalLoss, a multi-scale wrapper for segmentation loss computation
• Data loading utilities：
  • decollate_batch
  • PadListDataCollate with inverse support
• Support of slicing syntax for Dataset
• Initial Torchscript support for the loss modules
• Learning rate finder
• Allow for missing keys in the dictionary-based transforms
• Support of checkpoint loading for transfer learning
• Various summary and plotting utilities for Jupyter notebooks
• Contributor Covenant Code of Conduct
• Major CI/CD enhancements covering the tutorial repository
• Fully compatible with PyTorch 1.8
• Initial nightly CI/CD pipelines using Nvidia Blossom Infrastructure

Changed

• Enhanced list_data_collate error handling
• Unified iteration metric APIs
• densenet* extensions are renamed to DenseNet*
• se_res* network extensions are renamed to SERes*
• Transform base APIs are rearranged into compose, inverse, and transform
• _do_transform flag for the random augmentations is unified via RandomizableTransform
• Decoupled post-processing steps, e.g. softmax, to_onehot_y, from the metrics computations
• Moved the distributed samplers to monai.data.samplers from monai.data.utils
• Engine's data loaders now accept generic iterables as input
• Workflows now accept additional custom events and state properties
• Various type hints according to Numpy 1.20
• Refactored testing utility runtests.sh to have --unittest and --net integration tests options
• Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.02-py3 from nvcr.io/nvidia/pytorch:20.10-py3
• Docker images are now built with self-hosted environments
• Primary contact email updated to monai.contact@gmail.com
• Now using GitHub Discussions as the primary communication forum

Removed

• Compatibility tests for PyTorch 1.5.x
• Format specific loaders, e.g. LoadNifti, NiftiDataset
• Assert statements from non-test files
• from module import * statements, addressed flake8 F403

Fixed

• Uses American English spelling for code, as per PyTorch
• Code coverage now takes multiprocessing runs into account
• SmartCache with initial shuffling
• ConvertToMultiChannelBasedOnBratsClasses now supports channel-first inputs
• Checkpoint handler to save with non-root permissions
• Fixed an issue for exiting the distributed unit tests
• Unified DynUNet to have single tensor output w/o deep supervision
• SegmentationSaver now supports user-specified data types and a squeeze_end_dims flag
• Fixed *Saver event handlers output filenames with a data_root_dir option
• Load image functions now ensure little-endian
• Fixed the test runner to support regex-based test case matching
• Usability issues in the event handlers