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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SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.
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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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Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis
SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.