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: Jan 7, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation.

Jingyun Chen1, Yading Yuan1

  • 1Department of Radiation Oncology, Columbia University Irving Medical Center 622 W 168th St, New York, NY 10032, United States.

Proceedings of Spie--The International Society for Optical Engineering
|December 30, 2025
PubMed
Summary

Gossip Mutual Learning (GML) enhances decentralized medical AI by enabling peer-to-peer learning without a central server. This approach improves tumor segmentation accuracy and significantly reduces communication overhead compared to traditional federated learning methods.

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

HIT101308137 and HIT104293658 nominate dual target chemotypes for PTPN1 and PTPN2 with preliminary selectivity in colorectal cancer cells.

Frontiers in chemistry·2026
Same author

Mitigating Spurious Invariance in Contrastive Learning: A Probabilistic Self-Supervised Learning Framework for Medical Image Analysis.

IEEE transactions on medical imaging·2026
Same author

Employing an integrated computational simulation strategy to identify high-affinity ligands for TDP-43 amyloid proteins.

Bioorganic & medicinal chemistry·2026
Same author

A Phycoerythrin-SOD Fluorescent Probe Enables Detection of Oxidative Stress for Assessing Astaxanthin in NAFLD.

Antioxidants (Basel, Switzerland)·2026
Same author

Rumen-Protected Glucose Supplementation Enhances Yak Calf Growth Through Gut Microbiota-Metabolic Interactions.

Animals : an open access journal from MDPI·2026
Same author

Impaired LC-NE System-A Novel Molecular Mechanism Underlying Health Disparity and Increased Prevalence of Alzheimer's Disease Among African Americans.

Diagnostics (Basel, Switzerland)·2026

Area of Science:

  • Medical Imaging
  • Machine Learning
  • Artificial Intelligence

Background:

  • Federated learning (FL) enables collaborative model training across medical centers without data sharing.
  • Traditional FL's reliance on a central server presents a single point of failure and may not optimize for local data variations.
  • Site-specific data heterogeneity can limit the performance of globally trained models.

Purpose of the Study:

  • To introduce Gossip Mutual Learning (GML), a decentralized framework for collaborative machine learning in healthcare.
  • To address the limitations of centralized FL, including server vulnerability and suboptimal local performance.
  • To improve tumor segmentation accuracy on PET/CT images through a novel decentralized approach.

Main Methods:

  • Developed Gossip Mutual Learning (GML), a decentralized collaborative learning framework.
Keywords:
Federated learningGossip Mutual LearningScale Attention Networkdecentralized learningtumor segmentation

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

719

Related Experiment Videos

Last Updated: Jan 7, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

719
  • Utilized Gossip Protocol for direct peer-to-peer communication among clinical sites.
  • Implemented mutual learning where each site optimizes its local model using information from peers.
  • Evaluated GML on the HECKTOR21 dataset for tumor segmentation on PET/CT images.

    • Main Results:

    • GML improved tumor segmentation performance (Dice Similarity Coefficient) by 3.2%, 4.6%, and 10.4% compared to pooled training, FedAvg, and individual training, respectively.
    • GML demonstrated comparable generalization performance to pooled training and FedAvg on out-of-sample sites.
    • GML achieved a sixfold decrease in communication overhead compared to FedAvg, using only 16.67% of the communication cost.

    Conclusions:

    • GML offers a robust and efficient decentralized alternative to traditional federated learning for medical image analysis.
    • The GML framework enhances local model performance and reduces communication overhead, making it suitable for distributed medical AI applications.
    • Decentralized peer-to-peer learning effectively addresses data heterogeneity challenges in multi-center medical studies.