  B-mode ultrasound imaging is a popular medical imaging technique. Like other
image processing tasks, deep learning has been used for analysis of B-mode
ultrasound images in the last few years. However, training deep learning models
requires large labeled datasets, which is often unavailable for ultrasound
images. The lack of large labeled data is a bottleneck for the use of deep
learning in ultrasound image analysis. To overcome this challenge, in this work
we exploit Auxiliary Classifier Generative Adversarial Network (ACGAN) that
combines the benefits of data augmentation and transfer learning in the same
framework. We conduct experiment on a dataset of breast ultrasound images that
shows the effectiveness of the proposed approach.
