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.

翻译：医学超声（US）是临床实践中使用最广泛的成像模态之一，但其应用面临成像质量多变等独特挑战。深度学习（DL）模型可作为先进的医学超声图像分析工具，但其性能受到大规模数据集稀缺的严重制约。为解决常见的数据不足问题，我们开发了GSDA——一种基于生成对抗网络（GAN）的半监督数据增强方法。GSDA由GAN和卷积神经网络（CNN）组成。GAN可合成并生成高质量、高分辨率的超声图像及其伪标签，随后利用真实图像和合成图像共同训练CNN。为应对有限数据下GAN和CNN的训练难题，我们在其训练过程中引入迁移学习技术。我们还提出一种新的评估标准，在分类准确性与计算时间之间取得平衡。我们在BUSI数据集上评估了该方法，GSDA优于现有最先进方法。通过合成高分辨率、高质量的图像，GSDA仅用780张图像即可达到97.9%的准确率。鉴于这些令人鼓舞的结果，我们相信GSDA有望成为医学超声分析的辅助工具。