A Bayesian method constructs credible hyperrectangles from posteriors to compare high-dimensional correlation matrices in brain connectivity analysis, with theoretical guarantees under the inverse-Wishart model.
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2026 2verdicts
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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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Credible rectangles for high-dimensional posterior comparison
A Bayesian method constructs credible hyperrectangles from posteriors to compare high-dimensional correlation matrices in brain connectivity analysis, with theoretical guarantees under the inverse-Wishart model.
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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.