  With the success of deep learning-based methods applied in medical image
analysis, convolutional neural networks (CNNs) have been investigated for
classifying liver disease from ultrasound (US) data. However, the scarcity of
available large-scale labeled US data has hindered the success of CNNs for
classifying liver disease from US data. In this work, we propose a novel
generative adversarial network (GAN) architecture for realistic diseased and
healthy liver US image synthesis. We adopt the concept of stacking to
synthesize realistic liver US data. Quantitative and qualitative evaluation is
performed on 550 in-vivo B-mode liver US images collected from 55 subjects. We
also show that the synthesized images, together with real in vivo data, can be
used to significantly improve the performance of traditional CNN architectures
for Nonalcoholic fatty liver disease (NAFLD) classification.
