Neural network and statistical classification methods were applied to derive an objective grading for moderately and poorly differentiated lesions, based on characteristics of the nuclear placement patterns. Using a multilayer network after abbreviated training as a feature extractor followed by a quadratic Bayesian classifier allowed grade assignment agreeing with visual diagnostic consensus in 96% of fields from the training set of 500 fields, and a 77% of 130 fields of a test set
