Islam Jyoti, Zhang Yanqing
Department of Computer Science, Georgia State University, Atlanta, Georgia, 30302-5060, USA.
Brain Inform. 2020 Mar 30;7(1):3. doi: 10.1186/s40708-020-00104-2.
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).
近年来，深度学习技术在大规模标注数据集的帮助下，在与计算机视觉相关的任务中取得了巨大成功。获取用于医学图像分析的此类数据集极具挑战性。使用有限的数据集和少量标注样本进行工作，使得开发一个强大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方法。我们提出的模型可以为阿尔茨海默病的三个不同阶段——正常对照（NC）、轻度认知障碍（MCI）和阿尔茨海默病（AD）创建脑部PET图像。
