Author

Andrew J. Schaumberg

ORCID 0000-0001-7556-9208Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:imaging· 1

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Mark A. RubiniD· 1 shared Thomas J. FuchsiD· 1 shared

Activity by Year

2016-2016 · peak 1/yr

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2 top collaborators · 1 mutual co-author link

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1 shared paperPathology

Mark A. RubinORCID

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1 paper

Recent·Most cited

PathologybioRxivPreprintJul 21, 2016

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

Andrew J. Schaumberg, Mark A. Rubin, Thomas J. Fuchs

This paper develops a deep learning model that predicts SPOP mutation status in prostate cancer from H&E-stained whole slide images, achieving high accuracy across different slide types and demonstrating content-based image retrieval for genetic-histologic relationships.

DOI 10.1101/064279

humanimaging

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