- 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 src/exclusions.py (rules: total TV > 1e6 mm3, single target
< 100 mm3, non-brain NTUH keys) and scripts/make_tv_slices.py, a
first-stage curation script that classifies every labeled row right
after the source fetch and writes TV-slice QA figures to
data/tv_slices/{inclusion,exclusion}/.
05_build_splits.py re-applies the same rules after split assignment:
kept rows retain their split, excluded subjects still count in the
subject-level shuffle boundaries, and the audit trail is written to
data/manifests/excluded.jsonl. Label stats run in a
ProcessPoolExecutor (32 workers).
Move proc_row into src/common.py and add labeled_source_rows() so the
curation stage and the split builder share one processed-row view.
Document the 143-row exclusion audit and the interim 10-epoch
benchmark results in BENCHMARKING.md; update the README data layout
and script tables.
- Updated regex patterns for improved matching of series names and tags.
- Added functionality to reject non-head series based on study and series descriptions.
- Implemented max voxel spacing check to filter out series with excessive spacing.
- Enhanced the reconstruction script to handle dynamic-frame series exclusions and artifact pruning.
- Modified output paths for reconstructed NIfTI files and added QA screenshot generation.
- Improved argument parsing in benchmark and pseudo-labeling scripts for better flexibility.
- Introduced a new script for generating QA screenshots from reconstructed volumes.
Add a new benchmarking script and documentation to facilitate
performance comparisons between the different implemented pipelines.
- Create `scripts/benchmark_pipelines.py` for automated evaluation
- Add `BENCHMARKING.md` to outline benchmarking procedures and metrics
