Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1925
Title: Improved Detection of Breast Cancer Nuclei using Modular Neural Networks
Authors: Schnorrenberg, Frank 
Tsapatsoulis, Nicolas 
Pattichis, Constantinos S. 
Schizas, Christos N. 
Kollias, Stefanos D. 
Vassiliou, Mary 
Adamou, Adamos 
Kyriacou, Kyriacos C. 
Major Field of Science: Engineering and Technology
Keywords: Biomedical;Breast cancer Nuclei;Modular Neural Network
Issue Date: Jan-2000
Source: IEEE Engineering in Medicine and Biology Magazine, 2000, vol. 19, no. 1, pp.48-63
Volume: 19
Issue: 1
Start page: 48
End page: 63
Journal: IEEE Engineering in Medicine and Biology Magazine 
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.
URI: https://hdl.handle.net/20.500.14279/1925
ISSN: 07395175
DOI: 10.1109/51.816244
Rights: © IEEE Engineering in Medicine and Biology Society
Type: Article
Affiliation : University of Cyprus 
Appears in Collections:Άρθρα/Articles

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