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.
翻译:目前的医学成像合成增强技术依赖于大量使用基因对抗网络(GANs),然而,GAN结构的性质导致大量计算资源,用于制作合成图像,而增强过程需要多个阶段才能完成。为了应对这些挑战,我们引入了一种新的基因化元课程学习方法,对具体任务模式(学生)的端到端培训,只增加一个教师模式。教师学会编制课程,输入学生数据增强模式,并指导学生提高元学习模式的绩效。与GAN的生成者和歧视者相比,教师和学生相互竞争,以提高学生在目标任务上的绩效。关于组织病理学数据集的广泛实验表明,利用我们的框架可以显著和持续地改进分类绩效。