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Found 4 papers, 0 papers with code
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier
Standard classification methods based on handcrafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US).
Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments
We develop neural networks that can estimate the HK distribution parameters based on the signal-to-noise ratio, skewness and kurtosis calculated using fractional-order moments.
Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study
Attenuation coefficient (AC) is a fundamental measure of tissue acoustical properties, which can be used in medical diagnostics.
Breast mass segmentation based on ultrasonic entropy maps and attention gated U-Net
In comparison to commonly applied segmentation methods, which use US images, our approach is based on quantitative entropy parametric maps.
