A review of artificial intelligence in prostate cancer detection on imaging

I Bhattacharya, YS Khandwala… - … advances in urology, 2022‏ - journals.sagepub.com
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 …

[HTML][HTML] Advancements in mri-based radiomics and artificial intelligence for prostate cancer: A comprehensive review and future prospects

A Chaddad, G Tan, X Liang, L Hassan, S Rathore… - Cancers, 2023‏ - mdpi.com
Simple Summary The integration of artificial intelligence (AI) into radiomic models has
become increasingly popular due to advances in computer-aided diagnosis tools. These …

Application of swarm intelligence optimization algorithms in image processing: A comprehensive review of analysis, synthesis, and optimization

M Xu, L Cao, D Lu, Z Hu, Y Yue - Biomimetics, 2023‏ - mdpi.com
Image processing technology has always been a hot and difficult topic in the field of artificial
intelligence. With the rise and development of machine learning and deep learning …

[HTML][HTML] A flexible 2.5 D medical image segmentation approach with in-slice and cross-slice attention

A Kumar, H Jiang, M Imran, C Valdes, G Leon… - Computers in Biology …, 2024‏ - Elsevier
Deep learning has become the de facto method for medical image segmentation, with 3D
segmentation models excelling in capturing complex 3D structures and 2D models offering …

Fully automated deep learning model to detect clinically significant prostate cancer at MRI

JC Cai, H Nakai, S Kuanar, AT Froemming, CW Bolan… - Radiology, 2024‏ - pubs.rsna.org
Background Multiparametric MRI can help identify clinically significant prostate cancer
(csPCa)(Gleason score≥ 7) but is limited by reader experience and interobserver variability …

Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts?–a …

O Rouvière, T Jaouen, P Baseilhac… - Diagnostic and …, 2023‏ - Elsevier
Purpose The purpose of this study was to perform a systematic review of the literature on the
diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based …

RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate

W Shao, S Vesal, SJC Soerensen… - Computers in biology …, 2024‏ - Elsevier
Image registration can map the ground truth extent of prostate cancer from histopathology
images onto MRI, facilitating the development of machine learning methods for early …

Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study

A Karagoz, D Alis, ME Seker, G Zeybel, M Yergin… - Insights into …, 2023‏ - Springer
Objective To evaluate the effectiveness of a self-adapting deep network, trained on large-
scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in …

[HTML][HTML] Magnetic resonance imaging-based predictive models for clinically significant prostate cancer: a systematic review

M Triquell, M Campistol, A Celma, L Regis, M Cuadras… - Cancers, 2022‏ - mdpi.com
Simple Summary Magnetic resonance imaging (MRI) has allowed the early detection of PCa
to evolve towards clinically significant PCa (csPCa), decreasing unnecessary prostate …

ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases

CX Li, I Bhattacharya, S Vesal, P Ghanouni… - Medical Image …, 2025‏ - Elsevier
Abstract Magnetic Resonance Imaging (MRI) is increasingly being used to detect prostate
cancer, yet its interpretation can be challenging due to subtle differences between benign …