BDGAN: Boundary and Diversity-aware Generative Adversarial Network for Imbalanced Medical Image Augmentation

Abstract

Current deep learning-based medical image classification methods face challenges in effectively learning correct classification boundaries when dealing with imbalanced data. Traditional data augmentation methods often suffer from insufficient diversity, leading to limited performance improvement. Based on this, this paper proposes a Boundary and Diversity-aware Generative Adversarial Network (BDGAN) for data augmentation, focusing on class boundaries and intra-class diversity. First, to enhance the diversity of generated samples, we design a multi-generator GAN architecture, where each generator learns and generates different data patterns. Second, to further generate more diverse and higher-quality samples, we introduce mutual exclusion loss and Hausdorff loss. Finally, for downstream classification tasks, we design a sampling method based on One-Class SVM (OCS), enabling the GAN to focus more on training and generating boundary samples during the training process. Experimental results on two real-world medical image datasets demonstrate that the proposed method can generate more diverse and higher-quality augmented samples, effectively improving the performance of downstream classification tasks.