Interpretable GAN-based Breast Ultrasound Mass Augmentation for Improved Deep Learning BI-RADS Classification
Zhu Jie, Guo Shaoyong, Junfeng Ma, Pang Ting · Biomedical Data Science · 2025
Generative adversarial network (GAN) synthesizing images has been widely applied to data augmentation. However, existing GAN-based imaging generation has drawbacks in explaining the synthetic principles. Hence, generative approaches resolving the limited medical images for deep learning-based computer aided diagnosis (CAD) which requires large amounts of data are scanty. In this paper, we propose an interpretable data synthesis network using modified triple generative adversarial network deployed into deep learning-based breast imaging reporting and data system (BI-RADS) classification of breast ultrasound mass. The mass synthetic network consists of an interpretable GAN that grasps the standardized characterizations described in BI-RADS for mass. Then diagnostic network with synthetic data determines the BI-RADS level of breast mass based on multi-classifier. Experiments were performed on a private breast ultrasound mass dataset to show that the synthetic network can provide interpretability of generative mass. In addition, we demonstrate that the synthetic masses improve the performance of breast cancer classification.