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.
4
Pith papers citing it
representative citing papers
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.