# Enhanced Training Implementation Summary

## Completed Features

### ✅ Enhanced Training Script (`src/training/train_enhanced.py`)

**Core Improvements:**
- **Sliding Window ROI Configuration**: Automatically reads ROI size from `config["model"]["img_size"]`
- **CLI Overlap Control**: `--sw-overlap` flag for configurable overlap (default: 0.25)
- **Auto Channel Detection**: Infers `in_channels` from first training batch (robust for 4-channel brain MRI vs 1-channel CT)
- **Validation Overlays**: Optional visualization with `--save-overlays` and `--overlays-max`
- **Dynamic MLflow Import**: Safe optional logging (works without MLflow installed)

**New CLI Arguments:**
```bash
--sw-overlap 0.25          # Sliding window overlap for validation
--save-overlays            # Enable validation overlay generation
--overlays-max 5           # Max overlays to save per validation
```

**Example Usage:**
```bash
python src/training/train_enhanced.py \
  --config config/recipes/unetr_multimodal.json \
  --dataset-config config/datasets/msd_task01_brain.json \
  --sw-overlap 0.25 \
  --save-overlays \
  --overlays-max 5 \
  --amp
```

### ✅ Verification System

**MONAI Integration Checklist (`scripts/validation/verify_monai_checklist.py`):**
- Import validation for core MONAI components
- Isolated unit tests (transform presets)
- Isolated integration tests (MSD loader)
- CI-friendly execution (no plugin conflicts)

**Test Results:**
```
✅ MONAI loader import - PASSED
✅ Transform presets import - PASSED
✅ Unit tests - Transform presets - PASSED
✅ Integration tests - MONAI MSD loader - PASSED
```

### ✅ Documentation Updates

**Enhanced Documentation:**
- `src/training/README.md`: Detailed trainer documentation with CLI examples
- Main `README.md`: Updated training examples with new flags
- Enhanced feature descriptions and usage patterns

## Technical Implementation Details

### Sliding Window Inference
- Uses MONAI's `SlidingWindowInferer` with configurable ROI and overlap
- ROI size sourced from model config for consistency with training
- Fallback to full image dimensions when ROI not specified
- Type-safe implementation with `Tuple[int, int, int]` typing

### Auto Channel Detection
```python
def infer_in_channels_from_loader(loader: DataLoader, default: int = 4) -> int:
    """Inspect first batch to determine input channels robustly."""
    # Peeks at batch without consuming iterator
    # Handles both 4-channel (brain MRI) and 1-channel (CT) scenarios
```

### Validation Overlays
- Saves axial slice overlays (middle slice) during validation
- Gray background: First modality channel
- Green overlay: Ground truth segmentation
- Red overlay: Model predictions
- Configurable save location and quantity

### MLflow Integration
- Dynamic import pattern avoids hard dependency
- Graceful degradation when MLflow unavailable
- Logs training metrics, validation dice, and overlay artifacts

## Quality Assurance

### Code Quality
- ✅ All linting issues resolved (line length, imports, typing)
- ✅ Type hints for critical functions
- ✅ Proper error handling and graceful fallbacks
- ✅ Clean separation of concerns

### Testing
- ✅ Unit tests pass in isolation
- ✅ Integration tests pass with synthetic data
- ✅ Verification checklist runs successfully
- ✅ No import or syntax errors

### Documentation
- ✅ Comprehensive CLI documentation
- ✅ Usage examples in multiple locations
- ✅ Clear feature descriptions
- ✅ Integration with existing project documentation

## File Changes Summary

### Modified Files:
1. `src/training/train_enhanced.py` - Complete trainer implementation
2. `scripts/validation/verify_monai_checklist.py` - Enhanced verification script
3. `src/training/README.md` - Detailed trainer documentation
4. `README.md` - Updated examples and feature descriptions

### Key Features Delivered:
- [x] Sliding window ROI from config + CLI overlap control
- [x] Automatic in_channels detection from sample batch
- [x] Optional validation overlays with axial slice visualization
- [x] Robust testing framework with isolated execution
- [x] Comprehensive documentation updates

## Next Steps (Optional)

1. **Real Dataset Testing**: Test with actual MSD datasets
2. **Performance Optimization**: Profile training loop for bottlenecks
3. **Advanced Overlays**: Multi-slice or 3D visualization options
4. **Config Validation**: Schema validation for dataset/model configs
5. **TensorBoard Integration**: Alternative to MLflow for experiment tracking

## Usage Commands

**Run Verification:**
```bash
python scripts/validation/verify_monai_checklist.py
```

**Train with Enhanced Features:**
```bash
python src/training/train_enhanced.py \
  --config config/recipes/unetr_multimodal.json \
  --dataset-config config/datasets/msd_task01_brain.json \
  --sw-overlap 0.25 \
  --save-overlays \
  --overlays-max 5 \
  --amp
```

**Test Individual Components:**
```bash
# Unit tests only
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 PYTEST_ADDOPTS='' python -m pytest -q -c /dev/null tests/unit/test_transforms_presets.py

# Integration tests only
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 PYTEST_ADDOPTS='' python -m pytest -q -c /dev/null tests/integration/test_monai_msd_loader.py
```
