  Current medical image synthetic augmentation techniques rely on 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.
