Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histology slides obtained via biopsies. A major problem that stands in the way is lack of data for a few cancer-types. In this paper, we ascertain that data augmentation using GANs can be a viable solution to reduce the unevenness in the distribution of different cancer types in our dataset. Our demonstration showed that a dataset augmented to a 50% increase causes an increase in tumor detection from 80% to 87.5%

翻译：医学影像的进步是深度学习研究的重要组成部分。计算机视觉的目标之一是开发一个能够从活检获得的组织学切片中识别肿瘤的整体综合模型。目前面临的主要障碍是某些癌症类型的数据缺乏。本文证实，使用生成对抗网络进行数据增强可以作为一种可行方案，以减轻数据集中不同癌症类型分布不均的问题。我们的实验表明，将数据集扩充50%可使肿瘤检测率从80%提升至87.5%。