论文标题
使用生成的对抗网络和人工智能进行医学图像来对抗COVID-19
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review
论文作者
论文摘要
这篇综述介绍了一项有关甘斯在解决与COVID-19数据稀缺性和诊断相关的挑战中作用的全面研究。这是第一个总结了Covid-19的不同gans方法和肺部图像数据集的评论。它试图回答与gan的应用，流行的gan体系结构，常用图像模式以及源代码的可用性有关的问题。这项综述包括57项全文研究，这些研究报告了在Covid-19-19肺图像数据中使用GAN在不同应用中使用的。大多数研究（n = 42）都使用gan进行数据增强，以增强与19009诊断的AI技术的性能。 gan的其他流行应用是肺部的分割和肺部图像的超分辨率。自行车和条件gan是每个研究中最常用的架构。 29项研究使用了胸部X射线图像，而21个研究使用CT图像进行gan训练。对于大多数研究（n = 47），进行了实验并使用公开数据进行了报告。仅两项研究报告了放射科医生/临床医生对结果的次要评估。结论：研究表明，GAN具有很大的潜力来解决COVID-19的肺部图像的数据稀缺挑战。用GAN合成的数据有助于改善培训用于诊断Covid-19的卷积神经网络（CNN）模型的训练。此外，GAN还通过图像和分割的超分辨率提高了CNNS性能。这篇综述还确定了基于gan的方法在临床应用中的潜在转化的关键局限性。
This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data. Most of the studies (n=42) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and super-resolution of the lungs images. The cycleGAN and the conditional GAN were the most commonly used architectures used in nine studies each. 29 studies used chest X-Ray images while 21 studies used CT images for the training of GANs. For majority of the studies (n=47), the experiments were done and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only two studies. Conclusion: Studies have shown that GANs have great potential to address the data scarcity challenge for lungs images of COVID-19. Data synthesized with GANs have been helpful to improve the training of the Convolutional Neural Network (CNN) models trained for the diagnosis of COVID-19. Besides, GANs have also contributed to enhancing the CNNs performance through the super-resolution of the images and segmentation. This review also identified key limitations of the potential transformation of GANs based methods in clinical applications.
