论文标题
使用生成对抗网络生成组织病理学幻像图像，以改善肿瘤检测
Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection
论文作者
论文摘要
医学成像的进步是深度学习研究中的重要组成部分。计算机视觉的目标之一是开发整体，全面的模型，该模型可以从活检中识别组织学幻灯片中的肿瘤。阻碍障碍的一个主要问题是缺乏一些癌症类型的数据。在本文中，我们确定使用gan的数据扩大可能是减少数据集中不同癌症类型分布的不均匀性的可行解决方案。我们的演示表明，数据集增加到50％会导致肿瘤检测从80％增加到87.5％
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%
