Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase

Author:

Salvi MassimoORCID,Manini Claudia,López Jose I.,Fenoglio Dario,Molinari Filippo

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference58 articles.

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3. N.C. Blessin, C. Yang, T. Mandelkow, J.B. Raedler, W. Li, E. Bady, R. Simon, E. Vettorazzi, M. Lennartz, C. Bernreuther, Automated Ki-67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry, J Pathol. (n.d.).

4. Prostatic intraepithelial neoplasia: an overview;Brawer;Rev. Urol.,2005

5. Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M., 2010. The balanced accuracy and its posterior distribution. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, IEEE: pp. 3121–3124.

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