A Progressive Generative Adversarial Method for Structurally Inadequate Medical Image Data Augmentation
Ruixuan Zhang, Wenhuan Lu, Xi Wei, Jialin Zhu, Han Jiang, Zhi-Qiang Liu, Jie Gao, Xuewei Li, Jian Yu, Mei Yu, Ruiguo Yu
Abstract
The generation-based data augmentation method can overcome the challenge caused by the imbalance of medical image data to a certain extent. However, most of the current research focus on images with unified structure which are easy to learn. What is different is that ultrasound images are structurally inadequate, making it difficult for the structure to be captured by the generative network, resulting in the generated image lacks structural legitimacy. Therefore, a Progressive Generative Adversarial Method for Structurally Inadequate Medical Image Data Augmentation is proposed in this paper, including a network and a strategy. Our Progressive Texture Generative Adversarial Network alleviates the adverse effect of completely truncating the reconstruction of structure and texture during the generation process and enhances the implicit association between structure and texture. The Image Data Augmentation Strategy based on Mask-Reconstruction overcomes data imbalance from a novel perspective, maintains the legitimacy of the structure in the generated data, as well as increases the diversity of disease data interpretably. The experiments prove the effectiveness of our method on data augmentation and image reconstruction on Structurally Inadequate Medical Image both qualitatively and quantitatively. Finally, the weakly supervised segmentation of the lesion is the additional contribution of our method.