Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer

View ORCID ProfileAndrew J. Schaumberg, View ORCID ProfileMark A. Rubin, View ORCID ProfileThomas J. Fuchs
doi: https://doi.org/10.1101/064279
Andrew J. Schaumberg
aMemorial Sloan Kettering Cancer Center and the Tri-Institutional Training Program in Computational Biology and Medicine;
bWeill Cornell Graduate School of Medical Sciences;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Andrew J. Schaumberg
  • For correspondence: ajs625@cornell.edu rubinma@med.cornell.edu fuchst@mskcc.org
Mark A. Rubin
cCaryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital–Weill Cornell Medicine;
dSandra and Edward Meyer Cancer Center at Weill Cornell Medicine;
eDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mark A. Rubin
  • For correspondence: ajs625@cornell.edu rubinma@med.cornell.edu fuchst@mskcc.org
Thomas J. Fuchs
fDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center;
gDepartment of Pathology, Memorial Sloan Kettering Cancer Center
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Thomas J. Fuchs
  • For correspondence: ajs625@cornell.edu rubinma@med.cornell.edu fuchst@mskcc.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Article Information

doi 
https://doi.org/10.1101/064279
History 
  • October 1, 2018.

Article Versions

  • Version 1 (July 17, 2016 - 07:50).
  • Version 2 (July 18, 2016 - 07:50).
  • Version 3 (July 18, 2016 - 14:24).
  • Version 4 (July 21, 2016 - 09:05).
  • Version 5 (March 3, 2017 - 08:05).
  • Version 6 (March 27, 2017 - 09:21).
  • Version 7 (May 12, 2017 - 08:22).
  • Version 8 (May 12, 2017 - 13:22).
  • You are viewing Version 9, the most recent version of this article.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Andrew J. Schaumberga,b,*,
  2. Mark A. Rubinc,d,e,* and
  3. Thomas J. Fuchsf,g,*
  1. aMemorial Sloan Kettering Cancer Center and the Tri-Institutional Training Program in Computational Biology and Medicine;
  2. bWeill Cornell Graduate School of Medical Sciences;
  3. cCaryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital–Weill Cornell Medicine;
  4. dSandra and Edward Meyer Cancer Center at Weill Cornell Medicine;
  5. eDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine;
  6. fDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center;
  7. gDepartment of Pathology, Memorial Sloan Kettering Cancer Center
  1. ↵*To whom correspondence should be addressed. E-mail: ajs625{at}cornell.edu, rubinma{at}med.cornell.edu, fuchst{at}mskcc.org
Back to top
PreviousNext
Posted October 01, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer
Andrew J. Schaumberg, Mark A. Rubin, Thomas J. Fuchs
bioRxiv 064279; doi: https://doi.org/10.1101/064279
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer
Andrew J. Schaumberg, Mark A. Rubin, Thomas J. Fuchs
bioRxiv 064279; doi: https://doi.org/10.1101/064279

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Pathology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4113)
  • Biochemistry (8815)
  • Bioengineering (6519)
  • Bioinformatics (23462)
  • Biophysics (11789)
  • Cancer Biology (9209)
  • Cell Biology (13322)
  • Clinical Trials (138)
  • Developmental Biology (7436)
  • Ecology (11409)
  • Epidemiology (2066)
  • Evolutionary Biology (15150)
  • Genetics (10436)
  • Genomics (14043)
  • Immunology (9171)
  • Microbiology (22154)
  • Molecular Biology (8812)
  • Neuroscience (47569)
  • Paleontology (350)
  • Pathology (1428)
  • Pharmacology and Toxicology (2491)
  • Physiology (3730)
  • Plant Biology (8080)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6037)
  • Zoology (1253)