Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/133
Title: Improved Detection of Breast Cancer Nuclei using Modular Neural Networks
Authors: Schnorrenberg, Frank
Tsapatsoulis, Nicolas 
Pattichis, Constantinos 
Schizas, Christos N.
Kollias, Stefanos D. 
Vassiliou, Mary
Adamou, Adamos 
Kyriacou, Kyriacos C. ItemCrisRefDisplayStrategy.rp.deleted.icon
Keywords: Biomedical;Breast cancer Nuclei;Modular Neural Network
Issue Date: 2000
Publisher: IEEE Engineering in Medicine and Biology Society
Source: Engineering in Medicine and Biology Magazine, IEEE, Vol. 19, no. 1, (2000), pp.48-63
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.
URI: http://ktisis.cut.ac.cy/handle/10488/133
ISSN: 0739-5175
DOI: 10.1109/51.816244
Rights: © 2008 IEEE Engineering in Medicine and Biology Society
Type: Article
Appears in Collections:Άρθρα/Articles

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