Wolberg W H, Mangasarian O L
Department of Surgery, University of Wisconsin, Madison.
Anal Quant Cytol Histol. 1990 Oct;12(5):314-20.
Two computer-driven expert systems trained to correctly diagnose 369 fine needle aspirates of the breast on the basis of nine cytologic descriptive parameters were tested on 70 newly obtained aspirates (57 benign and 13 malignant). The system generated by multisurface pattern separation misclassified one malignant test sample (i.e., one false negative) while the system generated by a connectionist algorithm (neural network) misclassified two benign test samples (i.e., two false positives). A decision tree misclassified three of the benign test samples (i.e., three false positives). These expert systems aid in the cytologic diagnosis of breast aspirates and can serve as models for other applications.
两个基于九个细胞描述参数进行训练以正确诊断369例乳腺细针穿刺样本的计算机驱动专家系统，在70个新获取的穿刺样本（57个良性和13个恶性）上进行了测试。多表面模式分离生成的系统将一个恶性测试样本误分类（即一个假阴性），而由连接主义算法（神经网络）生成的系统将两个良性测试样本误分类（即两个假阳性）。决策树将三个良性测试样本误分类（即三个假阳性）。这些专家系统有助于乳腺穿刺样本的细胞学诊断，并可作为其他应用的模型。
