Compact Convolutional Neural Networks for Ultrasound Beamforming

We trained convolutional neural networks (CNNs) to suppress off-axis scattering in the short-time Fourier Transform (STFT) domain. Our training data were point target responses from simulated anechoic cysts. We used random neural architecture search to build CNN models with variable input formulations, layer sizes, and training hyperparameters. Our results showed that CNNs were easier to train, as they required fewer network weights to match the performance of fully-connected networks (FCNs). The best CNN models achieved comparable phantom CNRs with with two to three orders of magnitude fewer weights.