CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.
and Haueter, Ulrich and Massi-Benedetti, Massimo and Federici, Marco Orsini and Pieber, Thomas R
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Hybrid digital-twin matching plus Seq2Seq LSTM reduces 240-minute glucose forecast bias by 13.89 mg/dL and MAE by 28.62 mg/dL versus baselines on held-out T1DEXI data.
citing papers explorer
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CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans
CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.
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PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes
Hybrid digital-twin matching plus Seq2Seq LSTM reduces 240-minute glucose forecast bias by 13.89 mg/dL and MAE by 28.62 mg/dL versus baselines on held-out T1DEXI data.