College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China.
University of Canterbury, Christchurch 8140, New Zealand.
Oxid Med Cell Longev. 2022 Jun 14;2022:2129303. doi: 10.1155/2022/2129303. eCollection 2022.
The small size of labelled samples is one of the challenging problems in identifying early lung nodules from CT images using deep learning methods. Recent literature on the topic shows that deep convolutional generative adversarial network (DCGAN) has been used in medical data synthesis and gained some success, but does not demonstrate satisfactory results in synthesizing CT images. It primarily suffers from the problem of model convergence and is prone to mode collapse. In this paper, we propose a generative adversarial network (GAN) model with prior knowledge to generate CT images of early lung nodules from a small-size of labelled samples, i.e., SLS-PriGAN. Particularly, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning and acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. To validate our method, a general fast R-CNN network is trained using the CT images generated by SLS-PriGAN. The experiment result shows that it achieved a recognizing accuracy of 91%, a recall rate of 81%, and 1 score of 0.85 in identifying clinic CT images of early lung nodules. This provides a promising way of identifying early lung nodules from CT images using deep learning with small-size labelled samples.
标记样本的小尺寸是使用深度学习方法从 CT 图像中识别早期肺结节的挑战性问题之一。该主题的最新文献表明，深度卷积生成对抗网络（DCGAN）已被用于医学数据合成并取得了一些成功，但在合成 CT 图像方面并未显示出令人满意的结果。它主要存在模型收敛的问题，并且容易出现模式崩溃。在本文中，我们提出了一种具有先验知识的生成对抗网络（GAN）模型，用于从小尺寸标记样本中生成早期肺结节的 CT 图像，即 SLS-PriGAN。特别是，设计了一个知识获取网络和一个锐化网络用于先验知识的学习和获取，然后开发了一个 GAN 模型来生成早期肺结节的 CT 图像。为了验证我们的方法，使用 SLS-PriGAN 生成的 CT 图像训练了一个通用的快速 R-CNN 网络。实验结果表明，在识别临床 CT 图像中的早期肺结节时，它的识别准确率达到了 91%，召回率为 81%，1 分率为 0.85。这为使用深度学习从小尺寸标记样本中识别 CT 图像中的早期肺结节提供了一种很有前途的方法。
