title: A hybrid neural and statistical classifier system for histopathologic grading of prostatic lesions.
abstract: 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.
authors.affiliation: Lehrstuhl für Informatik V, Universität Mannheim, Mannheim, Germany.
authors.forename: R
authors.initials: R
authors.lastname: Stotzka
authors.forename: R
authors.initials: R
authors.lastname: Männer
authors.forename: P H
authors.initials: PH
authors.lastname: Bartels
authors.forename: D
authors.initials: D
authors.lastname: Thompson
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008432:Mathematical Computing; D016571:Neural Networks, Computer; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000145:classification / Q000473:pathology*; D012042:Registries