- Updated `05_monai_run_iterative.py` to include legacy mode and improved configuration options for training.
- Introduced `UNetBAligned` class in `monai_common.py` for deep supervision and instance normalization.
- Modified `train_transform` and `make_dataloader` in `monai_common.py` to support B-aligned data protocol with Z-score normalization and augmentation.
- Added new loss functions `nn_dc_and_ce` and `b_dc_and_ce` in `losses.py` for improved training performance.
- Enhanced `Unet3D` architecture in `unet3d.py` to support leaky ReLU and deep supervision.
- Created new augmentation functions in `augment_b.py` for Pipeline B's training, including rotation, scaling, and noise.
- Implemented new training scripts `run_b_100ep_stages.sh` and `run_c_bloss_100ep_after_b.sh` for structured training and evaluation.
- Updated dataset handling in `dataset.py` to accommodate new augmentation and normalization strategies.
Introduce the third segmentation pipeline using MONAI (1.6.x) to allow
direct comparison with Pipelines A and B. This includes the implementation
of the iterative pseudo-labeling workflow, training scripts, and
inference protocols.
- Add `scripts_monai/` directory containing the MONAI pipeline scripts.
- Update documentation in `README.md` and `AGENTS.md` to include MONAI
package requirements and pipeline details.
- Configure `.gitignore` to exclude MONAI-specific run directories.
- Update data directory descriptions to include MONAI pseudo-labels.
