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.
and Torre, Lindsey A
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Pre-trained VGG19 features with bagged decision tree selection and SVM classification achieve 0.994 AUC for mass vs non-mass detection on the INbreast mammogram dataset.
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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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Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier
Pre-trained VGG19 features with bagged decision tree selection and SVM classification achieve 0.994 AUC for mass vs non-mass detection on the INbreast mammogram dataset.