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Changelog

All notable changes to MONAI are documented in this file.

The format is based on Keep a Changelog.

Unreleased

1.0.0 - 2022-09-16

Added

  • monai.auto3dseg base APIs and monai.apps.auto3dseg components for automated machine learning (AutoML) workflow
  • monai.fl module with base APIs and MonaiAlgo for federated learning client workflow
  • An initial backwards compatibility guide
  • Initial release of accelerated MRI reconstruction components, including CoilSensitivityModel
  • Support of MetaTensor and new metadata attributes for various digital pathology components
  • Various monai.bundle enhancements for MONAI model-zoo usability, including config debug mode and get_all_bundles_list
  • new monai.transforms components including SignalContinuousWavelet for 1D signal, ComputeHoVerMaps for digital pathology, and SobelGradients for spatial gradients
  • VarianceMetric and LabelQualityScore metrics for active learning
  • Dataset API for real-time stream and videos
  • Several networks and building blocks including FlexibleUNet and HoVerNet
  • MeanIoUHandler and LogfileHandler workflow event handlers
  • WSIReader with the TiffFile backend
  • Multi-threading in WSIReader with cuCIM backend
  • get_stats API in monai.engines.Workflow
  • prune_meta_pattern in monai.transforms.LoadImage
  • max_interactions for deepedit interaction workflow
  • Various profiling utilities in monai.utils.profiling

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:22.08-py3 from nvcr.io/nvidia/pytorch:22.06-py3
  • Optionally depend on PyTorch-Ignite v0.4.10 instead of v0.4.9
  • The cache-based dataset now matches the transform information when read/write the cache
  • monai.losses.ContrastiveLoss now infers batch_size during forward()
  • Rearrange the spatial axes in RandSmoothDeform transforms following PyTorch's convention
  • Unified several environment flags into monai.utils.misc.MONAIEnvVars
  • Simplified __str__ implementation of MetaTensor instead of relying on the __repr__ implementation

Fixed

  • Improved error messages when both monai and monai-weekly are pip-installed
  • Inconsistent pseudo number sequences for different num_workers in DataLoader
  • Issue of repeated sequences for monai.data.ShuffleBuffer
  • Issue of not preserving the physical extent in monai.transforms.Spacing
  • Issue of using inception_v3 as the backbone of monai.networks.nets.TorchVisionFCModel
  • Index device issue for monai.transforms.Crop
  • Efficiency issue when converting the array dtype and contiguous memory

Deprecated

  • Addchannel and AsChannelFirst transforms in favor of EnsureChannelFirst
  • monai.apps.pathology.data components in favor of the corresponding components from monai.data
  • monai.apps.pathology.handlers in favor of the corresponding components from monai.handlers

Removed

  • Status section in the pull request template in favor of the pull request draft mode
  • monai.engines.BaseWorkflow
  • ndim and dimensions arguments in favor of spatial_dims
  • n_classes, num_classes arguments in AsDiscrete in favor of to_onehot
  • logit_thresh, threshold_values arguments in AsDiscrete in favor of threshold
  • torch.testing.assert_allclose in favor of tests.utils.assert_allclose

0.9.1 - 2022-07-22

Added

  • Support of monai.data.MetaTensor as core data structure across the modules
  • Support of inverse in array-based transforms
  • monai.apps.TciaDataset APIs for The Cancer Imaging Archive (TCIA) datasets, including a pydicom-backend reader
  • Initial release of components for MRI reconstruction in monai.apps.reconstruction, including various FFT utilities
  • New metrics and losses, including mean IoU and structural similarity index
  • monai.utils.StrEnum class to simplify Enum-based type annotations

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:22.06-py3 from nvcr.io/nvidia/pytorch:22.04-py3
  • Optionally depend on PyTorch-Ignite v0.4.9 instead of v0.4.8

Fixed

  • Fixed issue of not skipping post activations in Convolution when input arguments are None
  • Fixed issue of ignoring dropout arguments in DynUNet
  • Fixed issue of hard-coded non-linear function in ViT classification head
  • Fixed issue of in-memory config overriding with monai.bundle.ConfigParser.update
  • 2D SwinUNETR incompatible shapes
  • Fixed issue with monai.bundle.verify_metadata not raising exceptions
  • Fixed issue with monai.transforms.GridPatch returns inconsistent type location when padding
  • Wrong generalized Dice score metric when denominator is 0 but prediction is non-empty
  • Docker image build error due to NGC CLI upgrade
  • Optional default value when parsing id unavailable in a ConfigParser instance
  • Immutable data input for the patch-based WSI datasets

Deprecated

  • *_transforms and *_meta_dict fields in dictionary-based transforms in favor of MetaTensor
  • meta_keys, meta_key_postfix, src_affine arguments in various transforms, in favor of MetaTensor
  • AsChannelFirst and AddChannel, in favor of EnsureChannelFirst transform

0.9.0 - 2022-06-08

Added

  • monai.bundle primary module with a ConfigParser and command-line interfaces for configuration-based workflows
  • Initial release of MONAI bundle specification
  • Initial release of volumetric image detection modules including bounding boxes handling, RetinaNet-based architectures
  • API preview monai.data.MetaTensor
  • Unified monai.data.image_writer to support flexible IO backends including an ITK writer
  • Various new network blocks and architectures including SwinUNETR
  • DeepEdit interactive training/validation workflow
  • NuClick interactive segmentation transforms
  • Patch-based readers and datasets for whole-slide imaging
  • New losses and metrics including SurfaceDiceMetric, GeneralizedDiceFocalLoss
  • New pre-processing transforms including RandIntensityRemap, SpatialResample
  • Multi-output and slice-based inference for SlidingWindowInferer
  • NrrdReader for NRRD file support
  • Torchscript utilities to save models with meta information
  • Gradient-based visualization module SmoothGrad
  • Automatic regular source code scanning for common vulnerabilities and coding errors

Changed

  • Simplified TestTimeAugmentation using de-collate and invertible transforms APIs
  • Refactoring monai.apps.pathology modules into monai.handlers and monai.transforms
  • Flexible activation and normalization layers for TopologySearch and DiNTS
  • Anisotropic first layers for 3D resnet
  • Flexible ordering of activation, normalization in UNet
  • Enhanced performance of connected-components analysis using Cupy
  • INSTANCE_NVFUSER for enhanced performance in 3D instance norm
  • Support of string representation of dtype in convert_data_type
  • Added new options iteration_log, iteration_log to the logging handlers
  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:22.04-py3 from nvcr.io/nvidia/pytorch:21.10-py3
  • collate_fn generates more data-related debugging info with dev_collate

Fixed

  • Unified the spellings of "meta data", "metadata", "meta-data" to "metadata"
  • Various inaccurate error messages when input data are in invalid shapes
  • Issue of computing symmetric distances in compute_average_surface_distance
  • Unnecessary layer self.conv3 in UnetResBlock
  • Issue of torchscript compatibility for ViT and self-attention blocks
  • Issue of hidden layers in UNETR
  • allow_smaller in spatial cropping transforms
  • Antialiasing in Resize
  • Issue of bending energy loss value at different resolutions
  • kwargs_read_csv in CSVDataset
  • In-place modification in Metric reduction
  • wrap_array for ensure_tuple
  • Contribution guide for introducing new third-party dependencies

Removed

  • Deprecated nifti_writer, png_writer in favor of monai.data.image_writer
  • Support for PyTorch 1.6

0.8.1 - 2022-02-16

Added

  • Support of matshow3d with given channel_dim
  • Support of spatial 2D for ViTAutoEnc
  • Support of dataframe object input in CSVDataset
  • Support of tensor backend for Orientation
  • Support of configurable delimiter for CSV writers
  • A base workflow API
  • DataFunc API for dataset-level preprocessing
  • write_scalar API for logging with additional engine parameter in TensorBoardHandler
  • Enhancements for NVTX Range transform logging
  • Enhancements for set_determinism
  • Performance enhancements in the cache-based datasets
  • Configurable metadata keys for monai.data.DatasetSummary
  • Flexible kwargs for WSIReader
  • Logging for the learning rate schedule handler
  • GridPatchDataset as subclass of monai.data.IterableDataset
  • is_onehot option in KeepLargestConnectedComponent
  • channel_dim in the image readers and support of stacking images with channels
  • Skipping workflow run if epoch length is 0
  • Enhanced CacheDataset to avoid duplicated cache items
  • save_state utility function

Changed

  • Optionally depend on PyTorch-Ignite v0.4.8 instead of v0.4.6
  • monai.apps.mmars.load_from_mmar defaults to the latest version

Fixed

  • Issue when caching large items with pickle
  • Issue of hard-coded activation functions in ResBlock
  • Issue of create_file_name assuming local disk file creation
  • Issue of WSIReader when the backend is TiffFile
  • Issue of deprecated_args when the function signature contains kwargs
  • Issue of channel_wise computations for the intensity-based transforms
  • Issue of inverting OneOf
  • Issue of removing temporary caching file for the persistent dataset
  • Error messages when reader backend is not available
  • Output type casting issue in ScaleIntensityRangePercentiles
  • Various docstring typos and broken URLs
  • mode in the evaluator engine
  • Ordering of Orientation and Spacing in monai.apps.deepgrow.dataset

Removed

  • Additional deep supervision modules in DynUnet
  • Deprecated reduction argument for ContrastiveLoss
  • Decollate warning in Workflow
  • Unique label exception in ROCAUCMetric
  • Logger configuration logic in the event handlers

0.8.0 - 2021-11-25

Added

  • Overview of new features in v0.8
  • Network modules for differentiable neural network topology search (DiNTS)
  • Multiple Instance Learning transforms and models for digital pathology WSI analysis
  • Vision transformers for self-supervised representation learning
  • Contrastive loss for self-supervised learning
  • Finalized major improvements of 200+ components in monai.transforms to support input and backend in PyTorch and NumPy
  • Initial registration module benchmarking with GlobalMutualInformationLoss as an example
  • monai.transforms documentation with visual examples and the utility functions
  • Event handler for MLfLow integration
  • Enhanced data visualization functions including blend_images and matshow3d
  • RandGridDistortion and SmoothField in monai.transforms
  • Support of randomized shuffle buffer in iterable datasets
  • Performance review and enhancements for data type casting
  • Cumulative averaging API with distributed environment support
  • Module utility functions including require_pkg and pytorch_after
  • Various usability enhancements such as allow_smaller when sampling ROI and wrap_sequence when casting object types
  • tifffile support in WSIReader
  • Regression tests for the fast training workflows
  • Various tutorials and demos including educational contents at MONAI Bootcamp 2021

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.10-py3 from nvcr.io/nvidia/pytorch:21.08-py3
  • Decoupled TraceKeys and TraceableTransform APIs from InvertibleTransform
  • Skipping affine-based resampling when resample=False in NiftiSaver
  • Deprecated threshold_values: bool and num_classes: int in AsDiscrete
  • Enhanced apply_filter for spatially 1D, 2D and 3D inputs with non-separable kernels
  • Logging with logging in downloading and model archives in monai.apps
  • API documentation site now defaults to stable instead of latest
  • skip-magic-trailing-comma in coding style enforcements
  • Pre-merge CI pipelines now include unit tests with Nvidia Ampere architecture

Removed

  • Support for PyTorch 1.5
  • The deprecated DynUnetV1 and the related network blocks
  • GitHub self-hosted CI/CD pipelines for package releases

Fixed

  • Support of path-like objects as file path inputs in most modules
  • Issue of decollate_batch for dictionary of empty lists
  • Typos in documentation and code examples in various modules
  • Issue of no available keys when allow_missing_keys=True for the MapTransform
  • Issue of redundant computation when normalization factors are 0.0 and 1.0 in ScaleIntensity
  • Incorrect reports of registered readers in ImageReader
  • Wrong numbering of iterations in StatsHandler
  • Naming conflicts in network modules and aliases
  • Incorrect output shape when reduction="none" in FocalLoss
  • Various usability issues reported by users

0.7.0 - 2021-09-24

Added

  • Overview of new features in v0.7
  • Initial phase of major usability improvements in monai.transforms to support input and backend in PyTorch and NumPy
  • Performance enhancements, with profiling and tuning guides for typical use cases
  • Reproducing training modules and workflows of state-of-the-art Kaggle competition solutions
  • 24 new transforms, including
    • OneOf meta transform
    • DeepEdit guidance signal transforms for interactive segmentation
    • Transforms for self-supervised pre-training
    • Integration of NVIDIA Tools Extension (NVTX)
    • Integration of cuCIM
    • Stain normalization and contextual grid for digital pathology
  • Transchex network for vision-language transformers for chest X-ray analysis
  • DatasetSummary utility in monai.data
  • WarmupCosineSchedule
  • Deprecation warnings and documentation support for better backwards compatibility
  • Padding with additional kwargs and different backend API
  • Additional options such as dropout and norm in various networks and their submodules

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.08-py3 from nvcr.io/nvidia/pytorch:21.06-py3
  • Deprecated input argument n_classes, in favor of num_classes
  • Deprecated input argument dimensions and ndims, in favor of spatial_dims
  • Updated the Sphinx-based documentation theme for better readability
  • NdarrayTensor type is replaced by NdarrayOrTensor for simpler annotations
  • Self-attention-based network blocks now support both 2D and 3D inputs

Removed

  • The deprecated TransformInverter, in favor of monai.transforms.InvertD
  • GitHub self-hosted CI/CD pipelines for nightly and post-merge tests
  • monai.handlers.utils.evenly_divisible_all_gather
  • monai.handlers.utils.string_list_all_gather

Fixed

  • A Multi-thread cache writing issue in LMDBDataset
  • Output shape convention inconsistencies of the image readers
  • Output directory and file name flexibility issue for NiftiSaver, PNGSaver
  • Requirement of the label field in test-time augmentation
  • Input argument flexibility issues for ThreadDataLoader
  • Decoupled Dice and CrossEntropy intermediate results in DiceCELoss
  • Improved documentation, code examples, and warning messages in various modules
  • Various usability issues reported by users

0.6.0 - 2021-07-08

Added

  • 10 new transforms, a masked loss wrapper, and a NetAdapter for transfer learning
  • APIs to load networks and pre-trained weights from Clara Train Medical Model ARchives (MMARs)
  • Base metric and cumulative metric APIs, 4 new regression metrics
  • Initial CSV dataset support
  • Decollating mini-batch as the default first postprocessing step, Migrating your v0.5 code to v0.6 wiki shows how to adapt to the breaking changes
  • Initial backward compatibility support via monai.utils.deprecated
  • Attention-based vision modules and UNETR for segmentation
  • Generic module loaders and Gaussian mixture models using the PyTorch JIT compilation
  • Inverse of image patch sampling transforms
  • Network block utilities get_[norm, act, dropout, pool]_layer
  • unpack_items mode for apply_transform and Compose
  • New event INNER_ITERATION_STARTED in the deepgrow interactive workflow
  • set_data API for cache-based datasets to dynamically update the dataset content
  • Fully compatible with PyTorch 1.9
  • --disttests and --min options for runtests.sh
  • Initial support of pre-merge tests with Nvidia Blossom system

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.06-py3 from nvcr.io/nvidia/pytorch:21.04-py3
  • Optionally depend on PyTorch-Ignite v0.4.5 instead of v0.4.4
  • Unified the demo, tutorial, testing data to the project shared drive, and Project-MONAI/MONAI-extra-test-data
  • Unified the terms: post_transform is renamed to postprocessing, pre_transform is renamed to preprocessing
  • Unified the postprocessing transforms and event handlers to accept the "channel-first" data format
  • evenly_divisible_all_gather and string_list_all_gather moved to monai.utils.dist

Removed

  • Support of 'batched' input for postprocessing transforms and event handlers
  • TorchVisionFullyConvModel
  • set_visible_devices utility function
  • SegmentationSaver and TransformsInverter handlers

Fixed

  • Issue of handling big-endian image headers
  • Multi-thread issue for non-random transforms in the cache-based datasets
  • Persistent dataset issue when multiple processes sharing a non-exist cache location
  • Typing issue with Numpy 1.21.0
  • Loading checkpoint with both model and optmizier using CheckpointLoader when strict_shape=False
  • SplitChannel has different behaviour depending on numpy/torch inputs
  • Transform pickling issue caused by the Lambda functions
  • Issue of filtering by name in generate_param_groups
  • Inconsistencies in the return value types of class_activation_maps
  • Various docstring typos
  • Various usability enhancements in monai.transforms

0.5.3 - 2021-05-28

Changed

  • Project default branch renamed to dev from master
  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.04-py3 from nvcr.io/nvidia/pytorch:21.02-py3
  • Enhanced type checks for the iteration_metric handler
  • Enhanced PersistentDataset to use tempfile during caching computation
  • Enhanced various info/error messages
  • Enhanced performance of RandAffine
  • Enhanced performance of SmartCacheDataset
  • Optionally requires cucim when the platform is Linux
  • Default device of TestTimeAugmentation changed to cpu

Fixed

  • Download utilities now provide better default parameters
  • Duplicated key_transforms in the patch-based transforms
  • A multi-GPU issue in ClassificationSaver
  • A default meta_data issue in SpacingD
  • Dataset caching issue with the persistent data loader workers
  • A memory issue in permutohedral_cuda
  • Dictionary key issue in CopyItemsd
  • box_start and box_end parameters for deepgrow SpatialCropForegroundd
  • Tissue mask array transpose issue in MaskedInferenceWSIDataset
  • Various type hint errors
  • Various docstring typos

Added

  • Support of to_tensor and device arguments for TransformInverter
  • Slicing options with SpatialCrop
  • Class name alias for the networks for backward compatibility
  • k_divisible option for CropForeground
  • map_items option for Compose
  • Warnings of inf and nan for surface distance computation
  • A print_log flag to the image savers
  • Basic testing pipelines for Python 3.9

0.5.0 - 2021-04-09

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

0.4.0 - 2020-12-15

Added

  • Overview document for feature highlights in v0.4.0
  • Torchscript support for the net modules
  • New networks and layers:
    • Discrete Gaussian kernels
    • Hilbert transform and envelope detection
    • Swish and mish activation
    • Acti-norm-dropout block
    • Upsampling layer
    • Autoencoder, Variational autoencoder
    • FCNet
  • Support of initialisation from pretrained weights for densenet, senet, multichannel AHNet
  • Layer-wise learning rate API
  • New model metrics and event handlers based on occlusion sensitivity, confusion matrix, surface distance
  • CAM/GradCAM/GradCAM++
  • File format-agnostic image loader APIs with Nibabel, ITK readers
  • Enhancements for dataset partition, cross-validation APIs
  • New data APIs:
    • LMDB-based caching dataset
    • Cache-N-transforms dataset
    • Iterable dataset
    • Patch dataset
  • Weekly PyPI release
  • Fully compatible with PyTorch 1.7
  • CI/CD enhancements:
    • Skipping, speed up, fail fast, timed, quick tests
    • Distributed training tests
    • Performance profiling utilities
  • New tutorials and demos:
    • Autoencoder, VAE tutorial
    • Cross-validation demo
    • Model interpretability tutorial
    • COVID-19 Lung CT segmentation challenge open-source baseline
    • Threadbuffer demo
    • Dataset partitioning tutorial
    • Layer-wise learning rate demo
    • MONAI Bootcamp 2020

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:20.10-py3 from nvcr.io/nvidia/pytorch:20.08-py3

Backwards Incompatible Changes

  • monai.apps.CVDecathlonDataset is extended to a generic monai.apps.CrossValidation with an dataset_cls option
  • Cache dataset now requires a monai.transforms.Compose instance as the transform argument
  • Model checkpoint file name extensions changed from .pth to .pt
  • Readers' get_spatial_shape returns a numpy array instead of list
  • Decoupled postprocessing steps such as sigmoid, to_onehot_y, mutually_exclusive, logit_thresh from metrics and event handlers, the postprocessing steps should be used before calling the metrics methods
  • ConfusionMatrixMetric and DiceMetric computation now returns an additional not_nans flag to indicate valid results
  • UpSample optional mode now supports "deconv", "nontrainable", "pixelshuffle"; interp_mode is only used when mode is "nontrainable"
  • SegResNet optional upsample_mode now supports "deconv", "nontrainable", "pixelshuffle"
  • monai.transforms.Compose class inherits monai.transforms.Transform
  • In Rotate, Rotated, RandRotate, RandRotated transforms, the angle related parameters are interpreted as angles in radians instead of degrees.
  • SplitChannel and SplitChanneld moved from transforms.post to transforms.utility

Removed

  • Support of PyTorch 1.4

Fixed

  • Enhanced loss functions for stability and flexibility
  • Sliding window inference memory and device issues
  • Revised transforms:
    • Normalize intensity datatype and normalizer types
    • Padding modes for zoom
    • Crop returns coordinates
    • Select items transform
    • Weighted patch sampling
    • Option to keep aspect ratio for zoom
  • Various CI/CD issues

0.3.0 - 2020-10-02

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

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 - 2020-07-02

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 - 2020-04-17

Added

  • Public alpha source code release 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 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