Conceptual learning in an expert system for histopathologic diagnosis

Autonomous learning modules in a diagnostic expert system serve to reveal distributional properties of the diagnostic clue values. This leads to more exhaustive utilization of the collected information. It also results in matching the design granularity for the diagnostic discrimination to the true structure of the diagnostic data. Additional conceptual entities augment the knowledge base.

[1]  R L Shoemaker,et al.  Image understanding system for histopathology. , 1989, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[2]  P.H. Bartels,et al.  Unsupervised conceptual learning in a diagnostic expert system , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[3]  P H Bartels,et al.  Colonic lesion expert system. Performance evaluation. , 1988, Analytical and quantitative cytology and histology.

[4]  P H Bartels,et al.  Machine learning in quantitative histopathology. , 1988, Analytical and quantitative cytology and histology.

[5]  P. Bartels,et al.  Computer Analysis of Lymphocyte Images , 1980 .

[6]  R. L. Shoemaker,et al.  Organization And Knowledge Representation In An Expert System For Scene Segmentation In Histologic Sections , 1989, Photonics West - Lasers and Applications in Science and Engineering.

[7]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .