Learning Steatosis Staging With Two-Dimensional Convolutional Neural Networks: Comparison Of Accuracy Of Clinical B-Mode With A Co-Registered Spectrogram Representation Of Rf Data

We explore the potential of ultrasound raw data as an alternative for non-alcoholic fatty liver disease (NAFLD) staging based on two-dimensional Convolutional Neural Networks (CNNs). Learning is performed on a stacked spectral representation of RF data and compared to co-registered clinical B-mode patches. Our initial results show a superior accuracy with RF data compared to B-mode. Early steatosis stages were classified more accurately than advanced steatosis stages.