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.
Chen and Steven M
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A behavioral engagement scoring method predicts patient response propensity in care management using real-world data and supplies interpretable insights via prototypical patients without performance loss.
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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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Learning Patient Engagement in Care Management: Performance vs. Interpretability
A behavioral engagement scoring method predicts patient response propensity in care management using real-world data and supplies interpretable insights via prototypical patients without performance loss.