Hadjiiski L, Sahiner B, Chan H P, Petrick N, Helvie M
Department of Radiology, The University of Michigan, Ann Arbor 48109-0904, USA.
IEEE Trans Med Imaging. 1999 Dec;18(12):1178-87. doi: 10.1109/42.819327.
A new type of classifier combining an unsupervised and a supervised model was designed and applied to classification of malignant and benign masses on mammograms. The unsupervised model was based on an adaptive resonance theory (ART2) network which clustered the masses into a number of separate classes. The classes were divided into two types: one containing only malignant masses and the other containing a mix of malignant and benign masses. The masses from the malignant classes were classified by ART2. The masses from the mixed classes were input to a supervised linear discriminant classifier (LDA). In this way, some malignant masses were separated and classified by ART2 and the less distinguishable benign and malignant masses were classified by LDA. For the evaluation of classifier performance, 348 regions of interest (ROI's) containing biopsy proven masses (169 benign and 179 malignant) were used. Ten different partitions of training and test groups were randomly generated using an average of 73% of ROI's for training and 27% for testing. Classifier design, including feature selection and weight optimization, was performed with the training group. The test group was kept independent of the training group. The performance of the hybrid classifier was compared to that of an LDA classifier alone and a backpropagation neural network (BPN). Receiver operating characteristics (ROC) analysis was used to evaluate the accuracy of the classifiers. The average area under the ROC curve (A(z)) for the hybrid classifier was 0.81 as compared to 0.78 for the LDA and 0.80 for the BPN. The partial areas above a true positive fraction of 0.9 were 0.34, 0.27 and 0.31 for the hybrid, the LDA and the BPN classifier, respectively. These results indicate that the hybrid classifier is a promising approach for improving the accuracy of classification in CAD applications.
设计了一种结合无监督和监督模型的新型分类器，并将其应用于乳腺钼靶片上恶性和良性肿块的分类。无监督模型基于自适应共振理论（ART2）网络，该网络将肿块聚类为多个不同的类别。这些类别分为两种类型：一种仅包含恶性肿块，另一种包含恶性和良性肿块的混合。来自恶性类别的肿块由ART2进行分类。来自混合类别的肿块被输入到监督线性判别分类器（LDA）中。通过这种方式，一些恶性肿块由ART2分离并分类，而较难区分的良性和恶性肿块由LDA分类。为了评估分类器性能，使用了348个包含活检证实肿块（169个良性和179个恶性）的感兴趣区域（ROI）。使用平均73%的ROI进行训练，27%进行测试，随机生成了十个不同的训练和测试组划分。分类器设计，包括特征选择和权重优化，在训练组上进行。测试组与训练组保持独立。将混合分类器的性能与单独的LDA分类器和反向传播神经网络（BPN）的性能进行了比较。使用接收器操作特征（ROC）分析来评估分类器的准确性。混合分类器的ROC曲线下平均面积（A(z)）为0.81，而LDA为0.78，BPN为0.80。对于混合、LDA和BPN分类器，真阳性率高于0.9的部分面积分别为0.34、0.27和0.31。这些结果表明，混合分类器是提高CAD应用中分类准确性的一种有前途的方法。
