# Immediate Action Plan - Next Development Phase

**Date:** August 20, 2025
**Phase:** 3 - Performance Optimization & Advanced Features
**Timeline:** Next 2-4 weeks

## 🎯 High-Priority Next Steps

### 1. Model Performance Enhancement (Week 1-2)

#### Hyperparameter Optimization
- **Objective**: Improve model accuracy beyond baseline performance
- **Tools**: Integrate Optuna for automated hyperparameter search
- **Parameters to optimize**:
  - Learning rate schedule (cosine, warm restarts)
  - Batch size and accumulation steps
  - Augmentation parameters (rotation, intensity shifts)
  - Model architecture parameters (feature size, hidden dimensions)

**Implementation Plan**:
```bash
# Create optimization script
touch src/training/optimize_hyperparams.py
# Add Optuna to requirements
echo "optuna>=3.0.0" >> requirements.txt
# Create optimization config
touch config/optimization/hparam_search.json
```

#### Advanced Loss Functions
- **Current**: Standard Dice + Cross-entropy loss
- **Upgrade to**:
  - Focal Loss for hard example mining
  - Tversky Loss for precision/recall balance
  - Combined loss with learnable weights

### 2. Data Pipeline Improvements (Week 1)

#### Enhanced Augmentation Strategy
- **Medical-specific augmentations**:
  - Intensity scaling simulation (different MRI protocols)
  - Gaussian noise for robustness
  - Elastic deformations for anatomical variation
  - Multi-scale crop strategies

**Implementation**:
```python
# Add to src/data/transforms_medical.py
- RandomIntensityShift()
- SimulateLowResolution()
- ElasticDeformation()
- AnatomyAwareAugmentation()
```

#### Cross-Validation Framework
- **Current**: Single train/val split
- **Upgrade**: 5-fold cross-validation for robust evaluation
- **Benefits**: Better generalization assessment, statistical significance

### 3. Advanced Model Architectures (Week 2-3)

#### Transformer Integration
- **Add SegFormer**: State-of-the-art transformer for medical segmentation
- **Benefits**: Better long-range dependencies, improved accuracy
- **Implementation**:
  ```python
  # Add to src/models/architectures.py
  from transformers import SegformerForSemanticSegmentation
  ```

#### Ensemble Methods
- **Multi-model ensemble**: Combine UNETR + SegFormer + UNet
- **TTA enhancement**: Expand beyond simple flips (rotation, scaling)
- **Prediction fusion**: Learnable ensemble weights

### 4. Production Readiness (Week 3-4)

#### FastAPI Service
- **Goal**: Production-ready inference API
- **Features**:
  - Async processing for multiple concurrent requests
  - Queue management for batch processing
  - Health checks and monitoring endpoints
  - Swagger documentation

**File Structure**:
```
api/
├── main.py              # FastAPI application
├── models.py            # Pydantic request/response models
├── inference_service.py # Core inference logic
├── health.py            # Health check endpoints
└── docker/
    ├── Dockerfile       # Production container
    └── docker-compose.yml
```

#### Performance Optimization
- **Model optimization**:
  - TensorRT/ONNX conversion for faster inference
  - Mixed precision inference optimization
  - Memory usage profiling and optimization
- **Caching strategy**:
  - Model weight caching
  - Preprocessing cache for repeated inputs

## 🔧 Implementation Timeline

### Week 1: Foundation
- [ ] Set up Optuna hyperparameter optimization
- [ ] Implement advanced medical augmentations
- [ ] Create cross-validation framework
- [ ] Add focal loss and Tversky loss options

### Week 2: Advanced Models
- [ ] Integrate SegFormer architecture
- [ ] Implement ensemble prediction framework
- [ ] Enhanced TTA with rotation and scaling
- [ ] Performance benchmarking suite

### Week 3: Production API
- [ ] FastAPI service implementation
- [ ] Docker containerization
- [ ] Load testing and optimization
- [ ] Monitoring and logging setup

### Week 4: Optimization & Testing
- [ ] TensorRT/ONNX model conversion
- [ ] Memory and speed optimization
- [ ] Comprehensive integration testing
- [ ] Documentation and deployment guides

## 📋 Quick Wins (Can be done immediately)

### Enhanced Testing
```bash
# Add performance benchmarks
python -m pytest tests/performance/ -v
# Add memory usage tests
python -m pytest tests/memory/ -v
# Add integration tests for new features
python -m pytest tests/integration/test_advanced_features.py -v
```

### Configuration Management
- **Add environment-specific configs**:
  ```
  config/
  ├── environments/
  │   ├── development.json
  │   ├── staging.json
  │   └── production.json
  └── optimization/
      ├── hparam_search.json
      └── ensemble_config.json
  ```

### Monitoring Dashboard
- **Add training progress visualization**:
  - Real-time loss curves
  - Validation metric tracking
  - Overlay quality assessment
  - Resource utilization monitoring

## 🎯 Success Criteria

### Performance Targets
- **Dice Score**: Improve from current baseline to >0.85
- **Inference Speed**: <15 seconds per case (50% improvement)
- **Memory Usage**: <6GB GPU memory (25% reduction)
- **API Latency**: <30 seconds response time for single case

### Quality Metrics
- **Test Coverage**: Maintain >90% for new features
- **Documentation**: Complete API docs and deployment guides
- **Reliability**: >99% inference success rate
- **Clinical Validation**: Radiologist approval rating >4.5/5

## 🚀 Getting Started Commands

### Set up development environment for next phase:
```bash
# Install optimization dependencies
pip install optuna tensorrt-python onnx onnxruntime-gpu

# Create new directory structure
mkdir -p {api,config/optimization,tests/performance,tests/memory}

# Run baseline performance tests
python scripts/validation/benchmark_current_performance.py

# Start hyperparameter optimization
python src/training/optimize_hyperparams.py --config config/optimization/hparam_search.json
```

### Immediate validation:
```bash
# Test current system end-to-end
python src/inference/inference.py --help

# Run all current tests to ensure stability
python -m pytest tests/ -v --tb=short

# Generate performance baseline report
python scripts/utilities/generate_performance_report.py
```

---

**Estimated Development Time**: 3-4 weeks
**Priority Level**: High
**Dependencies**: None (all additive to current system)
**Risk Level**: Low (incremental improvements to stable base)
