Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Yamaguchi, Japan.
Adv Exp Med Biol. 2020;1213:47-58. doi: 10.1007/978-3-030-33128-3_3.
Image-based computer-aided diagnosis (CAD) algorithms by the use of convolutional neural network (CNN) which do not require the image-feature extractor are powerful compared with conventional feature-based CAD algorithms which require the image-feature extractor for classification of lung abnormalities. Moreover, computer-aided detection and segmentation algorithms by the use of CNN are useful for analysis of lung abnormalities. Deep learning will improve the performance of CAD systems dramatically. Therefore, they will change the roles of radiologists in the near future. In this article, we introduce development and evaluation of such image-based CAD algorithms for various kinds of lung abnormalities such as lung nodules and diffuse lung diseases.
基于图像的计算机辅助诊断 (CAD) 算法通过使用卷积神经网络 (CNN) 实现，无需使用图像特征提取器，与传统的基于特征的 CAD 算法相比，其具有强大的分类肺异常的能力，传统的基于特征的 CAD 算法需要使用图像特征提取器。此外，基于 CNN 的计算机辅助检测和分割算法对于分析肺异常非常有用。深度学习将极大地提高 CAD 系统的性能。因此，它们将在不久的将来改变放射科医生的角色。在本文中，我们介绍了各种肺异常（如肺结节和弥漫性肺疾病）的基于图像的 CAD 算法的开发和评估。
