Automatic PI-RADS assignment by means of formal methods

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment

Patrick Schelb, Simon Köhl, Jan Philipp Radtke et al. · Radiology · 2019 · 319 citations

Medicine, Radiology, Urology +17

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Andrea Tangherloni, Paolo Cazzaniga · Aisberg (University of Bergamo) · 2019 · 278 citations · Full text

Medicine, Radiology, Urology +10

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

Jing Wang, Chen‐Jiang Wu, Meiling Bao et al. · European Radiology · 2017 · 221 citations

Medicine, Oncology, Radiology +17

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values

David Bonekamp, Simon Köhl, Manuel Wiesenfarth et al. · Radiology · 2018 · 205 citations

Medicine, Radiology, Urology +17

Prostate Cancer: PI-RADS Version 2 Helps Preoperatively Predict Clinically Significant Cancers

Sung Yoon Park, Dae Chul Jung, Young Taik Oh et al. · Radiology · 2016 · 145 citations · Full text

Medicine, Surgery, Urology +17