Stotzka R, Männer R, Bartels P H, Thompson D
Lehrstuhl für Informatik V, Universität Mannheim, Mannheim, Germany.
Anal Quant Cytol Histol. 1995 Jun;17(3):204-18.
Neural network and statistical classification methods were applied to derive an objective grading for moderately and poorly differentiated lesions of the prostate, based on characteristics of the nuclear placement patterns. A partly trained multilayer neural network was used as a feature extractor. A hybrid classifier system using a quadratic Bayesian classifier applied to these features allowed grade assignment consensus with visual diagnosis in 96% of fields from a training set of 500 fields and in 77% of 130 fields of a test set.
基于细胞核排列模式的特征，应用神经网络和统计分类方法对前列腺中分化和低分化病变进行客观分级。一个部分训练的多层神经网络被用作特征提取器。将二次贝叶斯分类器应用于这些特征的混合分类器系统，在由500个视野组成的训练集中，96%的视野分级结果与视觉诊断一致；在由130个视野组成的测试集中，77%的视野分级结果与视觉诊断一致。
