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

Cross-Image Federated Learning for Hyperspectral Image Classification.

IEEE transactions on neural networks and learning systems·2026
Same author

A charge-reversal prodrug activated by tumor-acidity for selective cancer chemotherapy.

Biomaterials science·2026
Same author

ROS-Responsive Cationic Nanoparticles for Cyclosporine A Delivery in Dry Eye Disease: A Dual-Functional Nanotherapeutic Approach.

ACS omega·2026
Same author

PID: A Parameter-Efficient Isolation Domain-Incremental Learning Framework for Signal Modulation Classification.

IEEE transactions on neural networks and learning systems·2025
Same author

Feature refinement and rethinking attention for remote sensing image captioning.

Scientific reports·2025
Same author

Comparing efficacy and safety of low-dose versus standard-dose antiplatelet therapy in stroke patients: a meta-analysis.

Frontiers in pharmacology·2025
Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026
Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

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

Related Experiment Video

Updated: Jan 10, 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

DMformer: Difficulty-Adapted Masked Transformer for Semi-Supervised Medical Image Segmentation.

Zelin Peng, Guanchun Wang, Zhengqin Xu

    IEEE Journal of Biomedical and Health Informatics
    |November 26, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Difficulty-adapted Masked Transformer (DMformer) for semi-supervised medical image segmentation. DMformer enhances learning by adapting reconstruction difficulty, significantly improving segmentation accuracy with limited labeled data.

    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

    727
    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
    06:48

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

    Published on: January 7, 2019

    9.4K

    Related Experiment Videos

    Last Updated: Jan 10, 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

    727
    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
    06:48

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

    Published on: January 7, 2019

    9.4K

    Area of Science:

    • Medical Image Analysis
    • Computer Vision
    • Machine Learning

    Background:

    • Leveraging unlabeled data is crucial for semi-supervised medical image segmentation.
    • Shared human anatomy provides a strong prior for utilizing unlabeled medical images.
    • Masked image modeling inspires new approaches for incorporating anatomical priors.

    Purpose of the Study:

    • To develop a semi-supervised medical image segmentation framework that effectively utilizes unlabeled data through anatomical priors.
    • To introduce a difficulty-adapted mask mechanism to handle varying reconstruction complexities of different organs/tissues.
    • To improve the performance of medical image segmentation models using limited labeled data.

    Main Methods:

    • Incorporated an auxiliary unsupervised gross anatomy reconstruction task into a teacher-student framework.
    • Developed a difficulty-adapted mask mechanism by modulating masked region and class ratios.
    • Implemented region-based and class-based masking strategies tailored to reconstruction difficulty.
    • Utilized a conflict-aware gradient computation strategy to manage simultaneous factor modulation.
    • Built the Difficulty-adapted Masked Transformer (DMformer) upon vision transformers.

    Main Results:

    • DMformer demonstrated superior performance in semi-supervised medical image segmentation.
    • Achieved significant improvements in Dice Similarity Coefficient (DSC) on ACDC and Synapse datasets.
    • Outperformed state-of-the-art (SOTA) methods with 5% labeled images on ACDC (9.53% DSC improvement).
    • Outperformed SOTA methods with 30% labeled images on Synapse (4.63% DSC improvement).

    Conclusions:

    • The proposed difficulty-adapted mask mechanism effectively addresses varying reconstruction challenges in medical image segmentation.
    • DMformer offers a powerful approach for semi-supervised medical image segmentation, especially with limited labeled data.
    • The framework successfully leverages anatomical priors through an auxiliary reconstruction task, enhancing segmentation accuracy.