There is a growing piece of evidence that artificial intelligence may be helpful in the entire
prostate cancer disease continuum. However, building machine learning algorithms robust …

Background Radiomics promises to enhance the discriminative performance for clinically
significant prostate cancer (csPCa), but still lacks validation in real-life scenarios. This study …

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa
aggressiveness, for which a biopsy is required, is fundamental for patient management …

Simple Summary Computer-aided diagnosis systems to improve significant prostate cancer
(PCa) diagnoses are being reported in the literature. These methods are based on either …

Introduction This study aims to develop an imaging model based on multi-parametric MR
images for distinguishing between prostate cancer (PCa) and prostate hyperplasia. Methods …

Purpose A recently developed deep learning model (U-Net) approximated the clinical
performance of radiologists in the prediction of clinically significant prostate cancer (sPC) …

Background: Multiparametric magnetic resonance imaging (mpMRI) plays an important role
in the diagnosis of prostate cancer (PCa) in the current clinical setting. However, the …

Deep learning has the potential to drastically increase the accuracy and efficiency of
prostate cancer diagnosis, which would be of uttermost use. Today the diagnosis is …

Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve
the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep …

Background Prostate segmentation is an essential step in computer-aided detection and
diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good …