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Samuel A. Bobholz
at Medical College of Wisconsin
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Publications (5)
Proceedings Article | 4 April 2022 Poster
An automated approach for annotating Gleason patterns in whole-mount prostate cancer histology using deep learning
Proceedings Volume 12039, 120391L (2022) https://doi.org/10.1117/12.2610793
KEYWORDS: Prostate cancer, Statistical analysis, Prostate, Pathology, Neural networks, Machine learning, Computer aided diagnosis and therapy, Cancer
Proceedings Article | 15 February 2021 Presentation + Paper
Quantitative analysis of Alzheimer’s disease pathology in glioblastoma patients
M. Brehler, A. K. Lowman, S. Bobholz, S. D. McGarry, J. Connelly, E. Cochran, P. S. LaViolette
Proceedings Volume 11603, 116030B (2021) https://doi.org/10.1117/12.2580725
KEYWORDS: Brain, Tumors, Tissues, Positron emission tomography, Alzheimer's disease, Magnetic resonance imaging, Pathology, Image processing, Deconvolution
Proceedings Article | 15 February 2021 Presentation + Paper
Co-registration tool for large format whole mount prostate multi-plex histology
M. Brehler, A. K. Lowman, S. Bobholz, S. D. McGarry, A. Banerjee, K. Iczkowski, P. S. LaViolette
Proceedings Volume 11603, 116030T (2021) https://doi.org/10.1117/12.2580059
KEYWORDS: Image registration, Prostate, Tissues, Statistical analysis, Multiplexing, Image processing, Prostate cancer, Data acquisition
SPIE Journal Paper | 13 October 2020
Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network
John Bukowy, Halle Foss, Sean McGarry, Allison Lowman, Sarah Hurrell, Kenneth Iczkowski, Anjishnu Banerjee, Samuel Bobholz, Alexander Barrington, Alex Dayton, Jackson Unteriner, Kenneth Jacobsohn, William See, Marja Nevalainen, Andrew Nencka, Tyler Ethridge, David Jarrard, Peter LaViolette
JMI, Vol. 7, Issue 05, 057501, (October 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.5.057501
KEYWORDS: Image segmentation, Prostate, Magnesium, Biopsy, Prostate cancer, Tissues, Education and training, Deep learning, Networks, Machine learning
SPIE Journal Paper | 9 September 2020 Open Access
Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer
Sean McGarry, John Bukowy, Kenneth Iczkowski, Allison Lowman, Michael Brehler, Samuel Bobholz, Andrew Nencka, Alex Barrington, Kenneth Jacobsohn, Jackson Unteriner, Petar Duvnjak, Michael Griffin, Mark Hohenwalter, Tucker Keuter, Wei Huang, Tatjana Antic, Gladell Paner, Watchareepohn Palangmonthip, Anjishnu Banerjee, Peter LaViolette
JMI, Vol. 7, Issue 05, 054501, (September 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.5.054501
KEYWORDS: Tumor growth modeling, Statistical modeling, Prostate cancer, Magnetic resonance imaging, Tissues, Pathology, Cancer, Biopsy, Performance modeling, Data modeling
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