fastrad

Documentation

  • Introduction
    • Why fastrad?
    • Use Cases
  • Installation
    • Prerequisites
    • Installing from Source
    • Hardware Acceleration (CUDA)
      • cuCIM for GLSZM Acceleration
  • Quickstart: Evaluating a Lung Nodule
    • Prerequisites
    • Step 1: Load the Clinical Volume
    • Step 2: Configure the Extractor Blueprint
    • Step 3: Execute the Extraction
    • Step 4: Consume the Feature Output
    • Next Steps
    • Advanced: Hardware-Accelerated Voxel-wise Feature Extraction
  • User Guide
    • Core Philosophy: PyTorch over Sequences
    • Object-Orientated Architecture
    • Extraction Workflow Execution
      • VRAM Protection and Fallbacks
      • Handling Anisotropy
      • Single Voxel Limitations
  • Learn (Examples)
    • 01: Basic Feature Extraction
    • 02: PyTorch CUDA GPU Acceleration Benchmark
    • 03: Multi-Patient Clinical Batch Processing
    • 04: Advanced Configurations & Fallback Logic
  • Features & Mathematical Formulations
    • First Order Statistics
    • Shape (2D and 3D)
    • Gray Level Co-occurrence Matrix (GLCM)
    • Gray Level Size Zone Matrix (GLSZM)
    • Gray Level Dependence Matrix (GLDM)
    • Neighbourhood Gray Tone Difference Matrix (NGTDM)
  • Benchmarks
    • IBSI Compliance and Numerical Parity
    • GPU Performance
    • CPU Performance
      • Multi-threading Fairness Benchmark
      • ROI Size Scaling Benchmark (GPU)
    • Optimizing GLSZM (cuCIM)
    • Stability Guarantee
      • ICC Analysis on Real RIDER Scan-Rescan Pairs
      • Numerical Robustness to Input Perturbation
    • Memory Footprint Optimization
      • GPU VRAM Profile (Full Pipeline)
    • Edge Case Handling
    • Dense Voxel-Wise Hardware Extraction Performance
  • API Reference
    • MedicalImage
    • Mask
    • FeatureSettings
    • FeatureExtractor
    • DenseFeatureExtractor
  • Frequently Asked Questions (FAQ)
    • Feature Extraction: Input, Customization, and Reproducibility
    • Hardware and Errors
    • Common Exceptions
  • Developers Guide
    • Architecture: PyTorch over SimpleITK
    • Adding the Baseline (Feature Classes)
    • Scientific Parity Testing
    • Out-Of-Memory (OOM) GPU Catchers
  • Contributing to fastrad
    • The PR Process and Gotchas
    • Submitting Bug Reports
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