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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;
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  • 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;
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  • 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
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  • For correspondence: ajs625@cornell.edu rubinma@med.cornell.edu fuchst@mskcc.org
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Posted October 01, 2018.
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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
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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

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