Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Preventing complications in cardiac pacemaker therapy: a lifecycle-based risk management framework.

Frontiers in cardiovascular medicine·2026
Same author

Precise Regulation of Intrachannel Negative Charge Density in Metal-Organic Frameworks for Efficient Alkali-Ion Transport.

Angewandte Chemie (International ed. in English)·2026
Same author

Neural stem cell transplantation in rodent models of traumatic brain injury: a systematic review and meta-analysis.

Frontiers in bioengineering and biotechnology·2026
Same author

Improving Equitable Access to Antenatal Care in China: Challenges and Potential Innovative Approaches.

China CDC weekly·2026
Same author

ESR1 polymorphisms were associated with aromatase inhibitors induced musculoskeletal symptoms in breast cancer patients.

Breast cancer research and treatment·2026
Same author

Revolutionization of the Leadless Pacemaker Implantation: Hemodynamic Verification Slashes Vascular Complications.

Reviews in cardiovascular medicine·2026
Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024
Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024
See all related articles

Related Experiment Video

Updated: Dec 20, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

423

Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on

RuoXi Qin1, Huike Zhang2, LingYun Jiang1

  • 1PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China.

Computational and Mathematical Methods in Medicine
|May 27, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel domain adaptation method for lymph node CT image analysis. Our approach enhances diagnostic system performance across diverse data sources by preserving crucial class information.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.2K

Related Experiment Videos

Last Updated: Dec 20, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

423
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.2K

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Deep Learning

Background:

  • Computer-aided diagnosis (CAD) systems for lymph nodes often suffer performance degradation due to data variability from multicenter CT image sources.
  • Existing domain adaptation methods struggle with the unique challenges of large CT image sizes and complex data distributions in lymph node analysis.

Purpose of the Study:

  • To develop a robust domain adaptation technique for improving the performance of lymph node computer-aided diagnosis systems across multicenter CT data.
  • To address the variability adaptation problem specific to lymph node CT images by considering shared features and domain-specific conditioning information.

Main Methods:

  • Proposed a novel domain adaptation framework utilizing a cross-domain confounding representation to extract domain-invariant features.
  • Implemented a cycle-consistency learning framework to preserve class-conditioning information through cross-domain image translations.
  • Employed pixel-level cross-domain image mapping and semantic-level cycle consistency for stable confounding representation.

Main Results:

  • The proposed method achieved a significant improvement in accuracy, outperforming existing domain adaptation techniques by at least 4.4% on multicenter lymph node data.
  • Demonstrated the effectiveness of the cycle-consistency learning framework in preserving class-conditioning information for enhanced domain adaptation.
  • Validated the capability of the approach to handle complex feature distributions and achieve domain invariance.

Conclusions:

  • The developed domain adaptation method effectively addresses the challenges of multicenter lymph node CT data, leading to more robust and high-performance computer-aided diagnosis systems.
  • The combination of cross-domain confounding representation and cycle-consistency learning offers a promising direction for domain adaptation in medical imaging.
  • This approach provides a stable confounding representation with class-conditioning information, crucial for effective adaptation under complex feature distributions.