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

SSMSNet: Scribble-Supervised Myocardial Scar Segmentation in Late Gadolinium Enhancement Images.

Diagnostics (Basel, Switzerland)·2026
Same author

Updating the MASH pharmacotherapy landscape: a network meta-analysis incorporating SGLT2 inhibitors and emerging combination therapies.

Frontiers in endocrinology·2026
Same author

Enhancing the Prediction of Axillary Lymph Node Metastasis in Breast Cancer through Habitat-Based Radiomics and Voting Algorithms.

Ultrasound in medicine & biology·2025
Same author

M<sup>2</sup>UNet: Multi-Scale Feature Acquisition and Multi-Input Edge Supplement Based on UNet for Efficient Segmentation of Breast Tumor in Ultrasound Images.

Diagnostics (Basel, Switzerland)·2025
Same author

Dysregulated circular RNAs in rheumatoid arthritis: Cellular roles and clinical prospects.

Autoimmunity reviews·2025
Same author

Urinary albumin-to-creatinine ratio for predicting risk of all-cause mortality and specific-cause mortality in patients with rheumatoid arthritis: evidence from NHANES 1999-2018.

Clinical rheumatology·2024

Related Experiment Video

Updated: Jan 7, 2026

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

Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Joint Pre-Trained Fine-Tuning and Contrastive

Rong Huang1, Mengshi Tang2, Lin Pan2

  • 1The Academy of Digital China, Fuzhou University, Fuzhou 350001, China.

Bioengineering (Basel, Switzerland)
|December 30, 2025
PubMed
Summary

This study introduces a novel deep learning method using contrast-enhanced ultrasound (CEUS) to accurately predict breast cancer axillary lymph node metastasis (ALNM). The approach enhances diagnostic accuracy for early breast cancer detection and personalized treatment planning.

Keywords:
axillary lymph node metastasis assessmentbreast cancercontrast-enhanced ultrasoundcontrastive learningpre-trained fine-tuning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
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

470

Related Experiment Videos

Last Updated: Jan 7, 2026

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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
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

470

Area of Science:

  • Medical Imaging
  • Oncology
  • Artificial Intelligence

Background:

  • Accurate assessment of axillary lymph node metastasis (ALNM) is critical for breast cancer treatment.
  • Conventional ultrasound has limitations; contrast-enhanced ultrasound (CEUS) offers better visualization of microcirculation.
  • CEUS video analysis for ALNM is challenging due to data requirements and diagnostic expertise.

Purpose of the Study:

  • To develop an automated method for predicting breast cancer ALNM using CEUS video sequences.
  • To overcome limitations in generating large datasets for deep learning models in ALNM diagnosis.
  • To improve the efficiency and accuracy of ALNM assessment in breast cancer.

Main Methods:

  • A novel method combining pre-trained fine-tuning with contrastive learning for ALNM prediction.
  • Utilized a text-video contrastive learning framework with fine-tuning on a proprietary dataset.
  • Employed random prompt optimization and an adaptive fine-tuning optimizer for breast CEUS video analysis.

Main Results:

  • The proposed method achieved a sensitivity of 0.792 for ALNM prediction.
  • The method demonstrated a specificity of 0.8 for ALNM diagnosis.
  • Experimental results validate the effectiveness of the CEUS-based deep learning approach.

Conclusions:

  • The study successfully leveraged CEUS and deep learning for improved ALNM diagnosis.
  • The method shows potential to enhance early breast cancer screening accuracy.
  • This approach can contribute to more personalized treatment strategies for breast cancer patients.