Improved detection of breast cancer nuclei using modular neural networks

Autor:	Schnorrenberg, F., Tsapatsoulis, Nicolas, Pattichis, Constantinos S., Schizas, Christos N., Kollias, S., Vassiliou, M., Adamou, Adamos K., Kyriacou, Kyriacos C.
Přispěvatelé:	Schizas, Christos N. [0000-0001-6548-4980], Pattichis, Constantinos S. [0000-0003-1271-8151], Tsapatsoulis, Nicolas [0000-0002-6739-8602]
Rok vydání:	2000
Předmět:	Pathology Biomedical Biopsy receptive field Modular Neural Network progesterone receptor Image analysis Pattern Recognition Automated Immunoenzyme Techniques immunocytochemistry cancer diagnosis Image Processing Computer-Assisted Coloring Agents Hematoxylin Breast cancer nuclei Biopsy analysis support systems (BASS) Artificial neural network article General Medicine Computer simulation Immunohistochemistry Computer aided diagnosis Oncology Receptors Estrogen histopathology Engineering and Technology Female Medical imaging optical density Receptors Progesterone Algorithms estrogen receptor medicine.medical_specialty Biomedical Engineering Breast Neoplasms Biology Matrix algebra breast cancer Breast cancer medicine Humans human Cellular biophysics Cell Nucleus algorithm Feedforward neural networks business.industry Reproducibility of Results Cancer Neural Networks (Computer) Modular neural networks Modular design medicine.disease Modular neural network human tissue ROC Curve Neural Networks Computer business artificial neural network
Zdroj:	Europe PubMed Central IEEE Engineering in Medicine and Biology Magazine IEEE Eng.Med.Biol.Mag.
ISSN:	0739-5175
DOI:	10.1109/51.816244
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abde110b0c0667c43977cb7f457c3eea https://doi.org/10.1109/51.816244 Zobrazit plný text záznamu