In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer’s disease—normal control (NC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD).

中文翻译：

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基于GAN的合成脑PET图像生成

近年来，借助大规模带注释的数据集，深度学习技术在与计算机视觉相关的任务中取得了巨大的成功。获得用于医学图像分析的此类数据集非常具有挑战性。使用有限的数据集和少量带注释的样本会使开发健壮的自动化疾病诊断模型变得困难。我们提出了一种使用生成对抗网络（GAN）生成合成医学图像的新颖方法。我们提出的模型可以为阿尔茨海默氏病的三个不同阶段创建大脑PET图像-正常对照（NC），轻度认知障碍（MCI）和阿尔茨海默氏病（AD）。