Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study-Reference-Cited by-同舟云学术

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Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

Published:2016-03-09 Issue:4 Volume:29 Page:496-506
ISSN:0897-1889
Container-title:Journal of Digital Imaging
language:en
Short-container-title:J Digit Imaging

Author:

Mercan Ezgi,Aksoy Selim,Shapiro Linda G.,Weaver Donald L.,Brunyé Tad T.,Elmore Joann G.

Funder

Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

National Cancer Institute

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://link.springer.com/content/pdf/10.1007/s10278-016-9873-1.pdf

Reference49 articles.

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2. Lesgold A, Rubinson H, Feltovich P, Glaser R, Klopfer D, Wang Y: Expertise in a complex skill: Diagnosing x-ray pictures. Nat Exp 311–342: 1988

3. Vink JP, Van Leeuwen MB, Van Deurzen CHM, De Haan G: Efficient nucleus detector in histopathology images. J Microsc 2492:124–135, 2013

4. Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J: Mitosis detection in breast cancer histology images with deep neural networks. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8150 LNCS., 411–418, 2013

5. Irshad H, Jalali S, Roux L, Racoceanu D, Hwee LJ, Le NG, Capron F: Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach. J Pathol Inform 4(Suppl):S12, 2013

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