title: Machine learning in quantitative histopathology.
abstract: The role of expert systems functioning as process controllers in learning image understanding systems is discussed. Numeric learning systems already have found a number of applications in cytologic and histopathologic diagnosis. Depending on the required capabilities, systems of increasing complexity are needed. Expert systems to guide scene segmentation in histopathologic imagery require model-based reasoning. Diagnostic image interpretation with learning capability demands a full model of the human expert's competence, including a considerable variety of knowledge representation schemes and inference strategies, coordinated by a meta-process controller.
authors.affiliation: Department of Optical Sciences, University of Arizona, Tucson 85721.
authors.forename: P H
authors.initials: PH
authors.lastname: Bartels
authors.forename: J E
authors.initials: JE
authors.lastname: Weber
authors.forename: L
authors.initials: L
authors.lastname: Duckstein
mesh_terms: D001185:Artificial Intelligence; D003585:Cell Biology; D005103:Expert Systems; D006653:Histology; D007091:Image Processing, Computer-Assisted; D007858:Learning