Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning

Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning

Abstract Objectives To present an accurate machine-learning (ML) method and knowledge-based heuristics for automatic sequence-type identification in multi-centric multiparametric MRI (mpMRI) datasets for prostate cancer (PCa) ML. Methods Retrospective prostate mpMRI studies were classified into 5 se...

Full description

Saved in:
Bibliographic Details
Main Authors: José Guilherme de Almeida, Ana Sofia Castro Verde, Carlos Bilreiro, Inês Santiago, Joana Ip, Manolis Tsiknakis, Kostas Marias, Daniele Regge, Celso Matos, Nickolas Papanikolaou, ProCAncer-I
Format: Article
Language:English
Published: SpringerOpen 2025-03-01
Series:Insights into Imaging
Subjects:
Prostate
Prostatic Neoplasms
Multiparametric magnetic resonance imaging
Data curation
Supervised machine learning
Online Access:https://doi.org/10.1186/s13244-025-01938-2
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s13244-025-01938-2

Similar Items