Abstract: A computer-implemented method for training and using a neural network to predict a coherence function includes: training a neural network by mapping a plurality of different sets of training input samples to respective coherence function truths to generate a trained neural network; receiving an operational input sample; inputting the operational input sample into the trained neural network; obtaining, from the trained neural network, a coherence function mapped to the operational input sample in response to the inputting the operational input sample into the trained neural network; and executing a computer-based instruction based on obtaining the coherence function. The coherence function may be used to differentiate solid masses from fluid-filled masses.