Current medical image synthetic augmentation techniques rely on the intensive use of generative adversarial networks (GANs). However, the nature of GAN architecture leads to heavy computational resources to produce synthetic images and the augmentation process requires multiple stages to complete. To address these challenges, we introduce a novel generative meta curriculum learning method that trains the task-specific model (student) end-to-end with only one additional teacher model. The teacher learns to generate curriculum to feed into the student model for data augmentation and guides the student to improve performance in a meta-learning style. In contrast to the generator and discriminator in GAN, which compete with each other, the teacher and student collaborate to improve the student's performance on the target tasks. Extensive experiments on the histopathology datasets show that leveraging our framework results in significant and consistent improvements in classification performance.

Deep learning networks have demonstrated competitive performance for various tasks on medical images. However, obtaining promising results requires a large amount of annotated data for supervised training, which is labor-intensive. Recently, the increasing interest in transformers has suggested their robust performance on computer vision tasks, including generative adversarial networks (GANs). In this paper, we propose a conditional GAN built on pure transformer-based architectures, named MedViTGAN, to assist in generating synthetic histopathology images for data augmentation in an end-to-end manner. The presented model adopts a conditioned training strategy by incorporating a transformer-based auxiliary classifier to facilitate the discriminative image generation process. We further introduce an adaptive hybrid loss weighting mechanism to balance multiple losses over sources and classes to stabilize the training. Extensive experiments on the histopathology datasets show that leveraging MedViTGAN generated images results in a significant and consistent improvement in classification performance.