Advances in our understanding of the role of magnetic resonance imaging (MRI) for the
detection of prostate cancer have enabled its integration into clinical routines in the past two …

Objectives We systematically reviewed the current literature evaluating the ability of fully-
automated deep learning (DL) and semi-automated traditional machine learning (TML) MRI …

We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model 1 for
automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR …

Purpose To evaluate a novel method of semisupervised learning (SSL) guided by
automated sparse information from diagnostic reports to leverage additional data for deep …

A multitude of studies have explored the role of artificial intelligence (AI) in providing
diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection …

Automated methods for detecting prostate cancer and distinguishing indolent from
aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis …

Background Accurate classification of sites of interest on prostate-specific membrane
antigen (PSMA) positron emission tomography (PET) images is an important diagnostic …

The landscape of prostate cancer care has rapidly evolved. We have transitioned from the
use of conventional imaging, radical surgeries, and single-agent androgen deprivation …

Objectives To evaluate a fully automatic deep learning system to detect and segment
clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different …

Simple Summary In approximately 50% of prostate cancer patients undergoing surgical
treatment, cancer has extended beyond the prostate boundary (ie, extraprostatic extension) …