  Deep learning networks have demonstrated state-of-the-art performance on
medical image analysis tasks. However, the majority of the works rely heavily
on abundantly labeled data, which necessitates extensive involvement of domain
experts. Vision transformer (ViT) based generative adversarial networks (GANs)
recently demonstrated superior potential in general image synthesis, yet are
less explored for histopathology images. In this paper, we address these
challenges by proposing a pure ViT-based conditional GAN model for
histopathology image synthetic augmentation. To alleviate training instability
and improve generation robustness, we first introduce a conditioned class
projection method to facilitate class separation. We then implement a
multi-loss weighing function to dynamically balance the losses between
classification tasks. We further propose a selective augmentation mechanism to
actively choose the appropriate generated images and bring additional
performance improvements. Extensive experiments on the histopathology datasets
show that leveraging our synthetic augmentation framework results in
significant and consistent improvements in classification performance.
