Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Clinical Applications and Emerging Roles of Bone Wax in Orthopaedic Surgery: A Scoping Review.

Journal of clinical medicine·2026

Same author

Five-year outcomes of the PROVE-IT randomized controlled trial: Patient-Reported Outcomes of Robotic vs. Laparoscopic Ventral Hernia Repair with Intraperitoneal Mesh.

Surgical endoscopy·2026

Same author

Defining the Incremental Value of Endoscopic Ultrasound in Assessing Pancreatic Cystic Neoplasms.

Annals of surgery·2026

Same author

Screening of Monoclonal Antibodies from Integrated Phage and Mammalian Cell Display Libraries.

Methods in molecular biology (Clifton, N.J.)·2026

Same author

Spatial metabolomics and transcriptomics reveal the metabolic-immune niche associated with renal fibrosis in hyperuricemia.

Free radical biology & medicine·2026

Same author

Mastectomy pain blocks: A comparison of preoperative versus intraoperative pectoralis nerve blocks: Mastectomy pectoralis nerve blocks comparison.

American journal of surgery·2026

Search research articles

Related Experiment Video

Updated: Oct 27, 2025

Translational Orthotopic Models of Glioblastoma Multiforme

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

Deep cross-view co-regularized representation learning for glioma subtype identification.

Zhenyuan Ning¹, Chao Tu¹, Xiaohui Di¹

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.

Medical Image Analysis

|July 25, 2021

Summary

This study introduces a deep learning framework for identifying diffuse glioma subtypes using magnetic resonance imaging (MRI). The method enhances accuracy by integrating multiple imaging views for better tumor characterization.

Keywords:

Co-regularized Cross-view Deep representation learning Glioma subtype

More Related Videos

Author Spotlight: Enhanced Generation of Patient-Derived 3D Organoids for Glioblastoma and Glioma

Author Spotlight: Enhanced Generation of Patient-Derived 3D Organoids for Glioblastoma and Glioma

Published on: January 19, 2024

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Published on: April 12, 2020

Related Experiment Videos

Last Updated: Oct 27, 2025

Translational Orthotopic Models of Glioblastoma Multiforme

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

Author Spotlight: Enhanced Generation of Patient-Derived 3D Organoids for Glioblastoma and Glioma

Author Spotlight: Enhanced Generation of Patient-Derived 3D Organoids for Glioblastoma and Glioma

Published on: January 19, 2024

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Published on: April 12, 2020

Area of Science:

Neuro-oncology
Medical Imaging
Machine Learning

Background:

Diffuse gliomas are now classified by the World Health Organization (WHO) using genetic markers (e.g., isocitrate dehydrogenase, 1p/19q codeletion) alongside histology.
Accurate glioma subtype identification is crucial for guiding treatment decisions and assessing prognosis.
Magnetic resonance imaging (MRI) is widely used for glioma characterization, but tumor heterogeneity and variable imaging phenotypes present challenges in extracting discriminative features.

Purpose of the Study:

To develop a novel deep learning framework for accurate glioma subtype identification from MRI data.
To address the challenge of glioma heterogeneity and variable imaging phenotypes in MRI.
To integrate multi-view MRI information for improved feature representation learning.

Main Methods:

A deep cross-view co-regularized representation learning framework was proposed.
Latent view-specific representations were learned using bi-directional mapping and regularization techniques (view-correlated and output-consistent).
View-sharable representations were generated by projecting view-specific features into a shared space, enhanced by adversarial learning.

Main Results:

The proposed framework successfully integrated view-specific and view-sharable representations for glioma subtype identification.
Experimental results on multi-site datasets demonstrated superior performance compared to existing state-of-the-art methods.
The method showed significant improvements in detecting glioma subtype status.

Conclusions:

The developed deep cross-view co-regularized representation learning framework is effective for glioma subtype identification using MRI.
This approach enhances the ability to learn discriminative features from heterogeneous glioma imaging data.
The findings suggest a promising direction for improving diagnostic accuracy and clinical decision-making in glioma management.