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Research map / Architectures and efficiency / RNNs and normalization

SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data

AAAI 2023 · Architectures and efficiency › RNNs and normalization · approach: Scalable approximation and equivariant compute · 22 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SCI instead of HEVCBiomedical compression · HeartPSNR: 61.54 -> 63.23Helped
Use SCI instead of HEVCBiomedical compression · LungPSNR: 46.15 -> 45.63Hurt
Use SCI instead of HEVCBiomedical compression · KidneyPSNR: 50.09 -> 50.15Helped
Use SCI instead of HEVCBiomedical compression · BrainPSNR: 50.31 -> 50.82Helped
Use SCI instead of the strongest listed INR baselineBiomedical compression · NeuronsAccuracy (200): 0.9983 -> 0.9986Helped
Use SCI instead of the strongest listed INR baselineBiomedical compression · VesselsAccuracy (200): 0.9913 -> 0.9944Helped
Add adaptive spectrum-based partitioning rather than equidistant partitioning (ablation)SCI · Medical dataPSNR: 48.67 -> 49.29Helped
Add adaptive spectrum-based partitioning rather than no partitioning (ablation)SCI · Medical dataPSNR: 47.76 -> 49.29Helped
Use a wider first layer with fr>1 rather than fr=1.0 (ablation)SCI · Biological dataAccuracy (200): 0.9537 -> 0.9752Helped
Use a wider first layer with fr>1 rather than fr<1.0 (ablation)SCI · Biological dataAccuracy (200): 0.9354 -> 0.9752Helped
Allocate parameters proportional to block size and inverse spectrum concentration rather than block size only (ablation)SCI · Medical dataPSNR: 49.01 -> 49.29Helped
Allocate parameters proportional to block size and inverse spectrum concentration rather than inverse spectrum concentration only (ablation)SCI · Medical dataPSNR: 48.82 -> 49.29Helped

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