Park Sang Cheol, Pu Jiantao, Zheng Bin
Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, PA 15213, USA.
Acad Radiol. 2009 Mar;16(3):266-74. doi: 10.1016/j.acra.2008.08.012.
Global data-based and local instance-based machine-learning methods and classifiers have been widely used to optimize computer-aided detection and diagnosis (CAD) schemes to classify between true-positive and false-positive detections. In this study, the correlation between these two types of classifiers was investigated using a new independent testing data set, and the potential improvement of a CAD scheme's performance by combining the results of the two classifiers in detecting breast masses was assessed.
The CAD scheme first used image filtering and a multilayer topographic region growth algorithm to detect and segment suspicious mass regions. The scheme then used an image feature-based classifier to classify these regions into true-positive and false-positive regions. Two classifiers were used in this study. One was a global data-based machine-learning classifier, an artificial neural network (ANN), and the other was a local instance-based machine-learning classifier, a k-nearest neighbor (KNN) algorithm. An independent image database including 400 mammographic examinations was used in this study. Of these, 200 were cancer cases and 200 were negative cases. The preoptimized CAD scheme was applied twice to the database using the two different classifiers. The correlation between the two sets of classification results was analyzed. Three sets of CAD performance results using the ANN, KNN, and average detection scores from both classifiers were assessed and compared using the free-response receiver-operating characteristic method.
The results showed that the ANN achieved higher performance than the KNN algorithm, with a normalized area under the performance curve (AUC) of 0.891 versus 0.845. The correlation coefficients between the detection scores generated by the two classifiers were 0.436 and 0.161 for the true-positive and false-positive detections, respectively. The average detection scores of the two classifiers improved CAD performance and reliability by increasing the AUC to 0.912 and reducing the standard error of the estimated AUC by 14.4%. The detection sensitivity was also increased from 75.8% (ANN) and 65.9% (KNN) to 80.3% at a false-positive detection rate of 0.3 per image.
This study demonstrates that two global data-based and local data-based machine-learning classifiers (ANN and KNN) generated low correlated detection results and that combining the detection scores of these two classifiers significantly improved overall CAD performance (P < .01) and reduced standard error in CAD performance assessment.
基于全局数据和基于局部实例的机器学习方法及分类器已被广泛用于优化计算机辅助检测与诊断（CAD）方案，以区分真阳性和假阳性检测结果。在本研究中，使用一个新的独立测试数据集研究了这两种类型分类器之间的相关性，并评估了通过结合两种分类器在检测乳腺肿块时的结果来潜在提高CAD方案性能的情况。
CAD方案首先使用图像滤波和多层地形区域生长算法来检测和分割可疑肿块区域。然后该方案使用基于图像特征的分类器将这些区域分类为真阳性和假阳性区域。本研究使用了两种分类器。一种是基于全局数据的机器学习分类器，即人工神经网络（ANN），另一种是基于局部实例的机器学习分类器，即k近邻（KNN）算法。本研究使用了一个包含400例乳腺X线检查的独立图像数据库。其中，200例为癌症病例，200例为阴性病例。使用两种不同的分类器将预优化的CAD方案应用于该数据库两次。分析了两组分类结果之间的相关性。使用自由响应接收者操作特征方法评估并比较了使用ANN、KNN以及两种分类器平均检测分数的三组CAD性能结果。
结果显示，ANN的性能高于KNN算法，性能曲线下的归一化面积（AUC）分别为0.891和0.845。两种分类器生成的检测分数之间，真阳性检测的相关系数为0.436，假阳性检测的相关系数为0.161。两种分类器的平均检测分数通过将AUC提高到0.912并将估计AUC的标准误差降低14.4%，提高了CAD的性能和可靠性。在每张图像假阳性检测率为0.3时，检测灵敏度也从75.8%（ANN）和65.9%（KNN）提高到了80.3%。
本研究表明，两种基于全局数据和基于局部数据的机器学习分类器（ANN和KNN）产生的检测结果相关性较低，并且结合这两种分类器的检测分数可显著提高整体CAD性能（P <.01）并降低CAD性能评估中的标准误差。
