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14 changes: 12 additions & 2 deletions docs/source/whatsnew_0_6.md
Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
- Enhancements of the base metric interfaces
- C++/CUDA extension modules via PyTorch JIT compilation
- Backward compatibility and enhanced continuous integration/continuous delivery
- Collaboration with Project-MONAI/MONAILabel for smooth integration


## Decollating mini-batches as an essential post-processing step
Expand Down Expand Up @@ -32,8 +33,7 @@ To demonstrate this new feature, a medical image segmentation tutorial is create
It mainly produces the following figure to compare the loss curves and validation scores for
- training from scratch (the green line),
- applying pretrained MMAR weights without training (the magenta line),
- training from the MMAR model weights (the blue
line),
- training from the MMAR model weights (the blue line),

according to the number of training epochs:

Expand Down Expand Up @@ -62,3 +62,13 @@ New utilities are introduced on top of the existing semantic versioning modules,
At the same time, we actively analyze efficient, scalable, and secure CI/CD solutions to accommodate fast and collaborative codebase development.

Although a complete mechanism is still under development, these provide another essential step towards API-stable versions of MONAI, sustainable release cycles, and efficient open-source collaborations.

## Collaboration with [`Project-MONAI/MONAILabel`](https://github.com/Project-MONAI/MONAILabel) for smooth integration
Since MONAI v0.6, we welcome [`MONAILabel`](https://github.com/Project-MONAI/MONAILabel) under [`Project-MONAI`](https://github.com/Project-MONAI).

MONAI Label is an intelligent open source image labeling and learning tool that enables users to create annotated datasets and build AI annotation models for clinical evaluation.
MONAI Label enables application developers to build labeling apps in a serverless way,
where custom labeling apps are exposed as a service through the MONAI Label Server.

Please visit MONAILabel documentation website for details:
https://docs.monai.io/projects/label/en/latest/