Improved detection of breast cancer nuclei using modular neural networks

Author

Schnorrenberg, F. ; Tsapatsoulis, Nicolas ; Pattichis, C.S. ; Schizus, C.N. ; Kollias, S. ; Vassiliou, M. ; Adamou, A. ; Kyriacou, K.

Author_Institution

Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus

Volume

19

Issue

1

fYear

2000

Firstpage

48

Lastpage

63

Abstract

Discusses the analysis of nuclei in histopathological sections with a system that closely simulates human experts. The evaluation of immunocytochemically stained histopathological sections presents a complex problem due to many variations that are inherent in the methodology. In this respect, many aspects of immunocytochemistry remain unresolved, despite the fact that results may carry important diagnostic, prognostic, and therapeutic information. In this article, a modular neural network-based approach to the detection and classification of breast cancer nuclei stained for steroid receptors in histopathological sections is described and evaluated

Keywords

biochemistry; biological organs; cancer; cellular biophysics; gynaecology; image classification; medical image processing; neural nets; optical microscopy; biopsies; histopathological sections; human experts; immunocytochemically stained histopathological sections evaluation; important diagnostic information; improved breast cancer nuclei detection; medical diagnostic imaging; methodology variations; modular neural network-based approach; prognostic information; steroid receptors; therapeutic information; Analytical models; Biochemical analysis; Breast cancer; Chemical analysis; Chemical processes; Content addressable storage; Humans; Immune system; Neoplasms; Neural networks;

fLanguage

English

Journal_Title

Engineering in Medicine and Biology Magazine, IEEE

Publisher

ieee

ISSN

0739-5175

Type

jour

DOI

10.1109/51.816244

Filename

816244