Objective This study trains a U-shaped fully convolutional neural network (U-Net) model
based on peripheral contour measures to achieve rapid, accurate, automated identification …

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

Purpose Accurate detection of small renal masses (SRM) is a fundamental step for
automated classification of benign and malignant or indolent and aggressive renal tumors …

Artificial intelligence is used in predicting the clinical outcomes before minimally invasive
treatments for benign prostatic hyperplasia, to address the insufficient reliability despite …

Purpose To develop a cascaded deep learning model trained with apparent diffusion
coefficient (ADC) and T2-weighted imaging (T2WI) for fully automated detection and …

Background: Prostate cancer, which is associated with gland biology and also with
environmental risks, is a serious clinical problem in the male population worldwide …

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival
rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing …

Purpose To compare the inter-observer variability of apparent diffusion coefficient (ADC)
values of prostate lesions measured by 2D-region of interest (ROI) with and without specific …

Background In this work, we compare input level, feature level and decision level data fusion
techniques for automatic detection of clinically significant prostate lesions (csPCa). Methods …

Compared to two-dimensional (2D) ultrasound (US), three-dimensional (3D) US is a more
sensitive alternative that can provide quantitative information for monitoring neonatal …