Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI SpaceSean D. McGarry, Peter S. LaViolette, John D. Bukowy et al.|Tomography|2019Cited by 60
Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histologySavannah Duenweg, Peter S. LaViolette, Samuel Bobholz et al.|Journal of Pathology Informatics|2023Cited by 34
Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancerSean D. McGarry, Peter S. LaViolette, Kenneth A. Iczkowski et al.|Journal of medical imaging|2020Cited by 21
T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical RecurrenceSavannah Duenweg, Peter S. LaViolette, Samuel Bobholz et al.|Cancers|2023Cited by 17
Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histologySavannah Duenweg, Peter S. LaViolette, Michael Brehler et al.|PLoS ONE|2023Cited by 13