PyRadiomics is the de facto standard for radiomic feature extraction, but it's CPU-only and takes ~3 seconds per scan. At scale, that's a bottleneck.
I built fastrad — a PyTorch-native library that implements all 8 IBSI feature classes (first-order, shape 2D/3D, GLCM, GLRLM, GLSZM, GLDM, NGTDM) as native tensor operations. Everything runs on torch.Tensor with transparent device routing (auto/cuda/cpu).
Key numbers on an RTX 4070 Ti vs PyRadiomics:
• End-to-end: 0.116s vs 2.90s → 25× speedup
• Per-class gains range from 12.9× (GLRLM) to 49.3× (first-order)
• Single-thread CPU: 2.63× faster than PyRadiomics 32-thread on x86, 3.56× on Apple Silicon
• Peak VRAM: 654 MB
Correctness: validated against the IBSI Phase 1 digital phantom (105 features, max deviation ≤ 10⁻¹³%) and against PyRadiomics on a TCIA NSCLC CT — all 105 features agree to within 10⁻¹¹.
Happy to answer questions on the implementation — the GLCM and GLSZM kernels were the trickiest to get numerically identical to PyRadiomics.
Pre-print: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6436486
Github repo: https://github.com/helloerikaaa/fastrad
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