Refactor the data loading and preprocessing pipeline to handle edge cases in
medical imaging data, including NaN/Inf values, shape mismatches, and
numerical instability during training.
- Update `01_build_ntuh_manifest.py` with improved regex for T1c detection,
spine exclusion, and deduplication logic based on acquisition timestamps.
- Enhance `PatchDataset` in `src/dataset.py` to handle NaN/Inf values,
clip intensity ranges, and ensure label/image shape alignment via
padding/trimming.
- Add a zero-gradient fallback in `src/training.py` to prevent DDP
synchronization failures when encountering NaN/Inf losses.
- Add `scripts/scan_procs.py` for process monitoring.
- Increase DataLoader timeout to prevent hangs during heavy I/O.
