Author

S. A. Bobholz

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Approach:deep learning· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. K. Lowman· 1 shared A. Nencka· 1 shared A. Winiarz· 1 shared F. Kyereme· 1 shared K. A. Iczkowski· 1 shared M. Brehler· 1 shared P. LaViolette· 1 shared S. R. Duenweg· 1 shared

Activity by Year

2022-2022 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 28 mutual co-author links

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1 shared paperPathology

A. K. Lowman

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1 paper

Recent·Most cited

Comparison of machine learning to deep learning for automated annotation of Gleason patterns in whole mount prostate cancer histology.

S. R. Duenweg, M. Brehler, S. A. Bobholz, A. K. Lowman, A. Winiarz, F. Kyereme, A. Nencka, K. A. Iczkowski, P. LaViolette

This study compares machine learning and deep learning methods for Gleason pattern annotation in prostate cancer histology, finding that deep learning better differentiates Gleason patterns.

bioRxiv · Nov 10, 2022 · PreprintPathology