What’s new in 1.6.0 🎉🎉#

  • New losses: AUC-Margin Loss, Matthews Correlation Coefficient (MCC) Loss

    • AUC-Margin Loss: AUCMarginLoss directly optimizes the AUROC metric via a margin-based surrogate loss, enabling training workflows that target ranking-based performance rather than calibration-based objectives.

    • Matthews Correlation Coefficient (MCC) Loss: MCCLoss provides a differentiable loss based on the Matthews Correlation Coefficient, which accounts for all four confusion matrix categories and is particularly robust for imbalanced segmentation tasks.

  • New metric: EmbeddingCollapseMetric detects representational collapse in learned embedding spaces, useful for self-supervised and contrastive learning workflows in medical imaging to monitor embedding quality during training.

  • Whole Slide Image (WSI) reader now supports retrieval at a specified microns-per-pixel (MPP) resolution. This simplifies multi-scanner workflows where consistent physical-space resolution is required regardless of scanner magnification levels.

  • Nested dot-notation key access in ConfigParser.

  • Auto3DSeg algo serialization migrated from pickle to JSON for improved security and portability.

  • Global coordinates support in spatial crop transforms. These now support global coordinate mode, allowing crops to be specified in world/global coordinates rather than local image indices, improving interoperability with physical-space annotations.

  • SoftclDiceLoss and SoftDiceclDiceLoss enhanced with DiceLoss-compatible API

  • Variable expansion hardening has been added to the nnUNet app to eliminate code injection attacks when composing shell command lines, addressing concerns in GHSA-rghg-q7wp-9767.

  • NumpyReader has been updated with an allow_pickle boolean argument to enable/disable pickle loading from .npy/.npz files. This was previously hard-coded to be enabled, but is now defined by this argument and disabled by default. This addresses GHSA-qxq5-qhx6-94qw.

MONAI now tests for Python 3.10 onwards, having dropped version 3.9 which is now out of support. PyTorch 2.8 onwards is now supported only, older versions will likely continue to function.

Nested Dot-Notation Access in ConfigParser#

ConfigParser now supports nested dot-notation key access, making it easier to read and override deeply nested configuration values programmatically.

For example, accessing a value from the parser with parser["network_def.in_channels"] can instead be parser.network_def.in_channels. This feature supports indexing and assignment, eg. parser.network_def.in_channels[0] = 4 or parser.A.B["C"] = 99.

Auto3DSeg: JSON-Based Algo Serialization#

Auto3DSeg algorithm objects are now serialized using JSON instead of pickle. This removes a class of security risks associated with pickle deserialization and improves cross-environment portability of saved algorithm states. Using pickle for serialization can be re-enabled by setting the environment variable MONAI_ALLOW_PICKLE to 1 or the equivalent true value.

This was implemented to address GHSA-qxq5-qhx6-94qw.

SoftclDiceLoss / SoftDiceclDiceLoss API Alignment#

SoftclDiceLoss and SoftDiceclDiceLoss now accept the same arguments as DiceLoss, including reduction, smooth_nr, smooth_dr, and batch parameters, enabling drop-in use alongside the standard Dice loss in existing pipelines.

Minor Changes#

  • DiceMetric and DiceHelper accept additional parameters for finer control of reduction behavior

  • ExtractDataKeyFromMetaKeyd now works with MetaTensor inputs

  • ConvertToMultiChannelBasedOnBratsClasses supports configurable GD-enhancing tumor label

  • TorchScript compatibility: replaced Tensor | None union syntax with Optional[Tensor] across network modules

  • CrossAttentionBlock is now only instantiated when with_cross_attention=True, reducing memory overhead

  • GlobalMutualInformationLoss bin centers and LocalNormalizedCrossCorrelationLoss kernels registered as buffers for correct device handling (#8869, #8818)

  • NibabelReader avoids eager C-order memory copies, reducing peak RAM usage for large NIfTI files

  • Fixed align_corners mismatch in AffineTransform

  • Fixed nested Compose map_items behaviour in forward and inverse paths

  • Fixed anchor centering on grid cells in detection

  • Fixed multi-axis shear transform matrix composition

  • Fixed JukeboxLoss swapped input_amplitude/target_amplitude arguments

  • Fixed memory leak in optional_import traceback handling

  • Fixed RandSimulateLowResolution to use F.interpolate instead of set_track_meta

  • Fixed GPU memory leak when checking image/label device in engine utilities

  • Fixed AutoencoderKL proj_attn → out_proj key remapping in load_old_state_dict

  • Fixed incorrect truncated parameter in make_gaussian_kernel affecting LocalNormalizedCrossCorrelationLoss

  • Fixed compute_shape_offset non-tuple indexing for PyTorch ≥ 2.9

  • Auto3DSeg: fixed incorrect device resolution in analyzer and precomputed crop handling

  • Replaced np.random.* global calls with np.random.RandomState instances for reproducibility

  • Replaced BaseException with Exception across the codebase