论文标题
端到端生成元课程学习用于医疗数据增加
End to End Generative Meta Curriculum Learning For Medical Data Augmentation
论文作者
论文摘要
当前的医疗图像合成增强技术依赖于生成对抗网络（GAN）的密集使用。但是，GAN结构的性质导致了大量的计算资源来产生合成图像，并且增强过程需要多个阶段才能完成。为了应对这些挑战，我们介绍了一种新颖的生成元课程学习方法，该方法仅使用另外一个教师模型来训练特定于任务的模型（学生）。老师学会生成课程，以进食学生模型以进行数据增强，并指导学生以元学习方式提高表现。与GAN中的发电机和歧视者相反，彼此竞争的是，老师和学生合作，以提高学生在目标任务上的表现。关于组织病理学数据集的广泛实验表明，利用我们的框架会导致分类性能的显着和一致的改善。
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.
