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 Experiment Video

Updated: Jun 27, 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

An Adaptive Fusion Network for Breast Tumor Grading Based on Graph Structure Learning.

Lei Yang, Kang Li, Zhan Yu

    IEEE Journal of Biomedical and Health Informatics
    |May 25, 2026
    PubMed
    Summary
    This summary is machine-generated.

    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

    Preoperative prediction of lymphovascular and perineural invasion in locally advanced gastric cancer via CT habitat analysis and deep learning: A dual-center study.

    European journal of radiology·2026
    Same author

    Prediction of efficacy and prognosis of PD‑1/PD‑L1 inhibitor combination chemotherapy for gastric cancer using an AI model based on dual‑energy CT: A multicenter study.

    European journal of radiology·2026
    Same author

    Differential diagnosis of testicular embryonal rhabdomyosarcoma and testicular seminoma with enhanced CT: a retrospective study.

    Frontiers in oncology·2026
    Same author

    Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

    Academic radiology·2026
    Same author

    16S rRNA Gene Amplicon Sequencing of Gut Microbiota in Gestational Diabetes Mellitus and Type 2 Diabetes Mellitus.

    Journal of diabetes research·2026
    Same author

    A Segmentation-Guided Feature Alignment and Fusion Network for Glioma IDH Genotyping.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Privacy-Enhanced Vertical Federated Learning for Healthcare via Directional Noise and Subset Representations.

    IEEE journal of biomedical and health informatics·2026
    See all related articles

    This study introduces a novel multi-graph adaptive fusion network for accurate breast tumor grading using multi-modal data. The graph learning model enhances diagnostic accuracy for early breast cancer detection.

    Area of Science:

    • Oncology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Breast cancer poses a significant threat to women's health, necessitating accurate malignancy grading for improved survival rates.
    • Current clinical diagnosis relies on multi-modal data, but effectively integrating this information for grading remains a challenge.
    • Graph convolutional networks (GCNs) show promise for multi-modal data processing in tumor grading.

    Purpose of the Study:

    • To develop and evaluate a multi-graph adaptive fusion network for accurate breast tumor grading using multi-modal data.
    • To address the limitations of single-graph models in utilizing comprehensive multi-modal information for diagnosis.

    Main Methods:

    • A multi-modal information extraction network, comprising a graph structure learning layer and a graph auto-encoder, was designed to extract modal-specific information and graph structures.

    Related Experiment Videos

    Last Updated: Jun 27, 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

  • Attention scores were used to weight-fuse modal features and graphs, creating aggregated representations.
  • A neighborhood adaptive aggregation module was developed to optimize graph convolution by calculating neighborhood aggregation coefficients.

    • Main Results:

    • The proposed multi-graph adaptive fusion network effectively processed multi-modal medical data.
    • The graph learning-based diagnosis model demonstrated improved accuracy in multi-grading of breast tumors on both public and private datasets.

    Conclusions:

    • The developed multi-graph adaptive fusion network offers a robust approach for breast tumor grading.
    • This graph learning-based method enhances the accuracy of multi-modal data utilization in clinical diagnosis, potentially improving patient outcomes.