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.
Tzourio-Mazoyer, B
4 Pith papers cite this work. Polarity classification is still indexing.
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Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
A simple convolutional autoencoder on ADNI brain scans learns latent spaces linked to Alzheimer's progression; the new LRCP framework plus SHAP analysis identifies which atlas regions carry the clinically relevant information.
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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Fairboard: a quantitative framework for equity assessment of healthcare models
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
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Latent space projections and atlases: A cautionary tale in deep neuroimaging using autoencoders
A simple convolutional autoencoder on ADNI brain scans learns latent spaces linked to Alzheimer's progression; the new LRCP framework plus SHAP analysis identifies which atlas regions carry the clinically relevant information.
- CTseg: A Tool for Brain CT Segmentation, Spatial Normalisation, and Volumetrics