Improved detection of breast cancer nuclei using modular neural networks

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

dc.contributor.author	Schnorrenberg, F	en
dc.contributor.author	Tsapatsoulis, N	en
dc.contributor.author	Pattichis, C	en
dc.contributor.author	Schizus, C	en
dc.contributor.author	Kollias, S	en
dc.contributor.author	Vassiliou, M	en
dc.contributor.author	Adamou, A	en
dc.contributor.author	Kyriacou, K	en
dc.date.accessioned	2014-03-01T01:49:26Z
dc.date.available	2014-03-01T01:49:26Z
dc.date.issued	2000	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/25779
dc.subject	Steroid Receptor	en
dc.subject	Breast Cancer	en
dc.subject	Modular Neural Network	en
dc.title	Improved detection of breast cancer nuclei using modular neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/51.816244	en
heal.identifier.secondary	http://dx.doi.org/10.1109/51.816244	en
heal.publicationDate	2000	en
heal.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	en
heal.journalName	IEEE Pulse	en
dc.identifier.doi	10.1109/51.816244	en