Zhang W, Doi K, Giger M L, Nishikawa R M, Schmidt R A
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, Illinois 60637, USA.
Med Phys. 1996 Apr;23(4):595-601. doi: 10.1118/1.597891.
A shift-invariant artificial neutral network (SIANN) has been applied to eliminate the false-positive detections reported by a rule-based computer aided-diagnosis (CAD) scheme developed in our laboratory. Regions of interest (ROIs) were selected around the centers of the rule-based CAD detections and analyzed by the SIANN. In our previous study, background-trend correction and pixel-value normalization were used as the preprocessing of the ROIs prior to the SIANN. A ROI is classified as a positive ROI, if the total number of microcalcifications detected in the ROI is greater than a certain number. In this study, modifications were made to improve the performance of the SIANN. First, the preprocessing is removed because the result of the background-trend correction is affected by the size of ROIs. Second, image-feature analysis is employed to the output of the SIANN in an effort to eliminate some of the false detections by the SIANN. In order to train the SIANN to detect microcalcifications and also to extract image features of microcalcifications, the zero-mean-weight constraint and training-free-zone techniques have been developed. A cross-validation training method was also applied to avoid the overtraining problem. The performance of the SIANN was evaluated by means of ROC analysis using a database of 39 mammograms for training and 50 different mammograms for testing. The analysis yielded an average area under the ROC curve (A(z)) of 0.90 for the testing set. Approximately 62% of false-positive clusters detected by the rule-based scheme were eliminated without any loss of the true-positive clusters by using the improved SIANN with image feature analysis techniques.
一种平移不变人工神经网络（SIANN）已被用于消除我们实验室开发的基于规则的计算机辅助诊断（CAD）方案所报告的假阳性检测结果。在基于规则的CAD检测中心周围选择感兴趣区域（ROI），并通过SIANN进行分析。在我们之前的研究中，背景趋势校正和像素值归一化被用作SIANN之前对ROI的预处理。如果在ROI中检测到的微钙化总数大于某个数量，则将该ROI分类为阳性ROI。在本研究中，进行了改进以提高SIANN的性能。首先，去除预处理，因为背景趋势校正的结果受ROI大小的影响。其次，对SIANN的输出进行图像特征分析，以消除SIANN的一些错误检测。为了训练SIANN检测微钙化并提取微钙化的图像特征，开发了零均值权重约束和无训练区域技术。还应用了交叉验证训练方法来避免过训练问题。使用包含39幅乳腺X线照片用于训练和50幅不同乳腺X线照片用于测试的数据库，通过ROC分析评估SIANN的性能。对于测试集，分析得出ROC曲线下的平均面积（A(z)）为0.90。通过使用具有图像特征分析技术的改进SIANN，基于规则的方案检测到的约62%的假阳性簇被消除，而没有任何真阳性簇的损失。
