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Published April 12, 2021 | Version 0.5.0
Software Open

Project-MONAI/MONAI: 0.5.0

Authors/Creators

  • 1. NVIDIA
  • 2. @NVIDIA
  • 3. King's College London
  • 4. The University of Iowa
  • 5. Nvidia
  • 6. University of Iowa
  • 7. @SonoVol
  • 8. @AmigoLab
  • 9. HTIC
  • 10. @KCL-BMEIS
  • 11. SINAPSE Lab
  • 12. Microsoft Research
  • 13. Delivery Architecture & AI Strategy

Description

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

Files

Project-MONAI/MONAI-0.5.0.zip

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Additional details

Related works

Is supplement to
https://github.com/Project-MONAI/MONAI/tree/0.5.0 (URL)