Radiol. Dept., Georgetown Univ. Med. Center, Washington, DC.
IEEE Trans Med Imaging. 1996;15(2):206-17. doi: 10.1109/42.491422.
The authors have developed a neural-digital computer-aided diagnosis system, based on a parameterized two-level convolution neural network (CNN) architecture and on a special multilabel output encoding procedure. The developed architecture was trained, tested, and evaluated specifically on the problem of diagnosis of lung cancer nodules found on digitized chest radiographs. The system performs automatic "suspect" localization, feature extraction, and diagnosis of a particular pattern-class aimed at a high degree of "true-positive fraction" detection and low "false-positive fraction" detection. In this paper, the authors aim at the presentation of the two-level neural classification method in reducing false-positives in their system. They employed receiver operating characteristics (ROC) method with the area under the ROC curve (A(z)) as the performance index to evaluate all the simulation results. The two-level CNN showed superior performance (A(z)=0.93) to the single-level CNN (A(z)=0.85). The proposed two-level CNN architecture is proven to be promising and to be extensible, problem-independent, and therefore, applicable to other medical or difficult diagnostic tasks in two-dimensional (2-D) image environments.
作者开发了一种基于参数化两级卷积神经网络（CNN）架构和特殊多标签输出编码过程的神经数字计算机辅助诊断系统。该开发的架构是专门针对数字化胸片上发现的肺癌结节的诊断问题进行训练、测试和评估的。该系统执行自动“可疑”定位、特征提取和针对高“真阳性分数”检测和低“假阳性分数”检测的特定模式类别的诊断。在本文中，作者旨在介绍两级神经分类方法在降低系统中假阳性的作用。他们采用了接收器工作特征（ROC）方法，ROC 曲线下的面积（A(z)）作为性能指标来评估所有的模拟结果。两级 CNN 的性能（A(z)=0.93）优于单级 CNN（A(z)=0.85）。所提出的两级 CNN 架构被证明是有前途的、可扩展的、独立于问题的，因此适用于二维（2-D）图像环境中的其他医学或困难诊断任务。
