Medical Imaging Pipeline Analysis Framework

A comprehensive framework for analyzing medical imaging pipelines with GPU acceleration, CPU processing, and detailed performance comparisons. This project provides deep insights into medical image processing performance, utilization patterns, and optimization strategies.

Features

• GPU-Accelerated Analysis: Comprehensive GPU performance analysis with NVIDIA tools
• CPU Processing Analysis: CPU-only pipeline analysis for comparison
• Performance Comparison: Side-by-side GPU vs CPU performance metrics
• Medical Image Visualization: Multi-planar reconstruction, segmentation, and 3D rendering
• Utilization Monitoring: Real-time GPU/CPU utilization, memory, temperature, and power tracking
• Pipeline Deep Dive: Component-by-component analysis and optimization insights
• Augmentation Analysis: Complete medical image augmentation pipeline visualization

Prerequisites

• Python 3.8+
• CUDA-compatible GPU (for GPU analysis)
• NVIDIA drivers and CUDA toolkit
• Medical imaging datasets (MedNIST included)

Key Visualizations

Medical Imaging Visualizations

Multi-planar Reconstruction

Comprehensive multi-planar reconstruction showing Axial, Coronal, and Sagittal views with GPU acceleration

Segmentation Analysis

Advanced segmentation overlays with confidence mapping and GPU-accelerated processing

Medical Report Visualization

Complete medical imaging report with comprehensive analysis and visualization

Performance Analysis

GPU vs CPU Performance Dashboard

Side-by-side performance comparison showing GPU vs CPU metrics across all pipeline components

Efficiency Analysis

Comprehensive efficiency comparison including memory, power, and throughput optimization

Performance Comparison

Detailed performance metrics comparison with speedup ratios and efficiency gains

Radar Chart Analysis

Multi-dimensional analysis showing GPU vs CPU performance across different metrics

Winning Solution Analysis

Advanced GPU analysis demonstrating winning solution performance metrics and optimization strategies

Installation

1. Clone the repository:

git clone https://github.com/kopalgarg/medical-imaging-pipeline.git
cd medical-imaging-pipeline

2. Install dependencies:

pip install -r requirements.txt

3. Verify GPU setup (optional):

nvidia-smi

Quick Start

1. Download MedNIST Dataset

python download_mednist.py

2. Run GPU Analysis

python comprehensive_gpu_analysis.py

3. Run CPU Analysis

python comprehensive_cpu_analysis.py

4. Run GPU vs CPU Comparison

python enhanced_gpu_cpu_comparison.py

Analysis Components

GPU Analysis (comprehensive_gpu_analysis.py)

• Enhanced Visualizations: Multi-planar reconstruction, segmentation overlays, 3D rendering
• Utilization Patterns: Real-time GPU utilization, memory usage, temperature monitoring
• Augmentation Analysis: Complete augmentation pipeline with quality metrics
• Pipeline Deep Dive: Component analysis, performance bottlenecks, optimization insights
• Performance Metrics: Batch processing, throughput, memory efficiency, power efficiency

CPU Analysis (comprehensive_cpu_analysis.py)

• CPU-Optimized Visualizations: Multi-planar views, segmentation, processing pipeline
• CPU Utilization: Core utilization patterns, memory usage, temperature monitoring
• CPU Augmentation: Augmentation pipeline optimized for CPU processing
• Pipeline Analysis: CPU component breakdown, performance characteristics
• Performance Metrics: CPU batch processing, throughput, efficiency analysis

Comparison Analysis (enhanced_gpu_cpu_comparison.py)

• Performance Comparison: Side-by-side performance dashboards
• Utilization Comparison: Real-time utilization tracking comparison
• Augmentation Comparison: Pipeline timing, quality trade-offs, speedup analysis
• Pipeline Comparison: Component-by-component GPU vs CPU analysis
• Efficiency Analysis: Memory, power, and throughput efficiency comparisons

Results Structure & File Paths

All analysis results are organized in the comprehensive_gpu_cpu_analysis_results/ directory with the following structure:

comprehensive_gpu_cpu_analysis_results/
├── gpu_results/              # GPU analysis results
│   ├── visualizations/       # GPU visualizations
│   │   ├── gpu_enhanced_multiplanar.png      # Multi-planar reconstruction
│   │   ├── gpu_segmentation_analysis.png     # Segmentation analysis
│   │   ├── demo_medical_report_visualization.png # Medical report demo
│   │   └── demo_comparison_visualization.png  # Comparison demo
│   ├── utilization_patterns/ # GPU utilization analysis
│   ├── augmentation_analysis/ # GPU augmentation analysis
│   ├── pipeline_deep_dive/   # GPU pipeline analysis
│   ├── performance_analysis/ # GPU performance metrics
│   ├── reports/              # GPU analysis reports
│   ├── metrics/              # GPU performance data
│   └── winning_solution_analysis.png          # Winning solution analysis
├── cpu_results/              # CPU analysis results
│   ├── visualizations/       # CPU visualizations
│   ├── utilization_patterns/ # CPU utilization analysis
│   ├── augmentation_analysis/ # CPU augmentation analysis
│   ├── pipeline_deep_dive/   # CPU pipeline analysis
│   ├── performance_analysis/ # CPU performance metrics
│   ├── reports/              # CPU analysis reports
│   └── metrics/              # CPU performance data
└── comparison_results/       # GPU vs CPU comparison results
    ├── performance_comparison/ # Performance comparison plots
    │   ├── gpu_cpu_performance_dashboard.png  # Main performance dashboard
    │   └── gpu_cpu_performance_stats.json     # Performance statistics
    ├── utilization_comparison/ # Utilization comparison analysis
    ├── augmentation_comparison/ # Augmentation comparison
    ├── pipeline_comparison/   # Pipeline comparison analysis
    ├── reports/               # Comparison reports
    ├── metrics/               # Comparison metrics
    └── visualizations/        # Comparison visualizations
        ├── gpu_cpu_efficiency_analysis.png    # Efficiency analysis
        ├── gpu_cpu_performance_comparison.png # Performance comparison
        └── gpu_cpu_radar_chart.png           # Radar chart analysis

Advanced Usage

Custom Analysis Configuration

# Modify analysis parameters in config.py
from config import *

# Custom GPU analysis
analyzer = ComprehensiveGPUAnalyzer()
analyzer.create_custom_analysis()

# Custom CPU analysis
analyzer = ComprehensiveCPUAnalyzer()
analyzer.create_custom_analysis()

# Custom comparison
comparator = EnhancedGPUCPUComparator()
comparator.create_custom_comparison()

Integration with Custom Pipelines

# Import your custom pipeline
from your_pipeline import YourPipeline

# Analyze custom pipeline
pipeline = YourPipeline()
analyzer.analyze_custom_pipeline(pipeline)

Batch Processing

# Run all analyses in sequence
python comprehensive_gpu_analysis.py && \
python comprehensive_cpu_analysis.py && \
python enhanced_gpu_cpu_comparison.py

Performance Metrics

GPU Metrics

• Utilization: GPU compute and memory utilization
• Throughput: Images processed per second
• Memory Efficiency: GPU memory usage optimization
• Power Efficiency: Watts per image processed
• Temperature: Thermal performance monitoring

CPU Metrics

• Core Utilization: Multi-core CPU usage patterns
• Memory Bandwidth: RAM usage and efficiency
• Processing Speed: CPU-only processing performance
• Power Consumption: CPU power efficiency
• Thermal Performance: CPU temperature monitoring

Comparison Metrics

• Speedup Ratio: GPU vs CPU performance improvement
• Efficiency Gain: Resource utilization improvement
• Cost Analysis: Performance per watt comparison
• Scalability: Multi-device performance scaling

Troubleshooting

Common Issues

1. GPU Not Detected

   nvidia-smi  # Check GPU status
   python -c "import torch; print(torch.cuda.is_available())"  # Check PyTorch CUDA

2. Memory Issues

   # Reduce batch size in config.py
   BATCH_SIZE = 16  # Instead of 32 or 64

3. Import Errors

   pip install -r requirements.txt --force-reinstall

4. Performance Issues

   # Check GPU utilization
   watch -n 1 nvidia-smi
   
   # Monitor CPU usage
   htop

Performance Optimization

1. GPU Optimization

   • Use mixed precision training
   • Optimize batch sizes
   • Enable tensor cores
   • Monitor memory usage

2. CPU Optimization

   • Utilize multi-threading
   • Optimize memory access patterns
   • Use CPU-optimized libraries
   • Implement batch processing