Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images-Reference-Cited by-同舟云学术

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images

Published:2022-12 Issue:12 Volume:35 Page:1983-1990
ISSN:0893-3952
Container-title:Modern Pathology
language:en
Short-container-title:Modern Pathology

Author:

Farahani Hossein^ORCID,Boschman Jeffrey^ORCID,Farnell David,Darbandsari Amirali,Zhang Allen,Ahmadvand Pouya,Jones Steven J.M.^ORCID,Huntsman David,Köbel Martin,Gilks C. Blake,Singh Naveena^ORCID,Bashashati Ali^ORCID

Funder

Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Michael Smith Foundation for Health Research

Cascadia Data Alliance, OVCARE Carraresi, UBC ObGyn funds

Publisher

Elsevier BV

Subject

Pathology and Forensic Medicine

Link

https://www.nature.com/articles/s41379-022-01146-z.pdf

Reference33 articles.

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4. Poor interobserver reproducibility in the diagnosis of high-grade endometrial carcinoma;Gilks;Am J Surg Pathol,2013

5. Veliparib with first-line chemotherapy and as maintenance therapy in ovarian cancer;Coleman;N. Engl J Med,2019

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