Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate CancerSean D. McGarry, Peter S. LaViolette, Sarah Hurrell et al.|International Journal of Radiation Oncology*Biology*Physics|2018Cited by 70
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
Optimized b-value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imagingSarah Hurrell, Alexander C. Mackinnon, Sean D. McGarry et al.|Journal of medical imaging|2017Cited by 48
Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancerSean D. McGarry, Peter S. LaViolette, John D. Bukowy et al.|Journal of medical imaging|2020Cited by 21
Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural networkJohn D. Bukowy, Peter S. LaViolette, Halle Foss et al.|Journal of medical imaging|2020Cited by 12