  Medical Ultrasound (US) is one of the most widely used imaging modalities in
clinical practice, but its usage presents unique challenges such as variable
imaging quality. Deep Learning (DL) models can serve as advanced medical US
image analysis tools, but their performance is greatly limited by the scarcity
of large datasets. To solve the common data shortage, we develop GSDA, a
Generative Adversarial Network (GAN)-based semi-supervised data augmentation
method. GSDA consists of the GAN and Convolutional Neural Network (CNN). The
GAN synthesizes and pseudo-labels high-resolution, high-quality US images, and
both real and synthesized images are then leveraged to train the CNN. To
address the training challenges of both GAN and CNN with limited data, we
employ transfer learning techniques during their training. We also introduce a
novel evaluation standard that balances classification accuracy with
computational time. We evaluate our method on the BUSI dataset and GSDA
outperforms existing state-of-the-art methods. With the high-resolution and
high-quality images synthesized, GSDA achieves a 97.9% accuracy using merely
780 images. Given these promising results, we believe that GSDA holds potential
as an auxiliary tool for medical US analysis.
