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

Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Corrigendum to "Sendai virus-based immunoadjuvant in hydrogel vaccine intensity-modulated dendritic cells activation for suppressing tumorigenesis" [Bioact. Mater. 6 (2021) 3879-3891].

Bioactive materials·2025
Same author

Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.

Schizophrenia (Heidelberg, Germany)·2025
Same author

Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.

Journal of neural engineering·2025
Same author

SEEG Emotion Recognition Based on Transformer Network With Channel Selection and Explainability.

IEEE journal of biomedical and health informatics·2025
Same author

The Cortical Spatial Responses and Decoding of Emotion Imagery Toward a Novel fNIRS-Based Affective BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Oct 5, 2025

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

3.0K

Semi-Supervised 3D Medical Image Segmentation Based on Dual-Task Consistent Joint Learning and Task-Level

Qi-Qi Chen, Zhao-Hui Sun, Chuan-Feng Wei

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 21, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel semi-supervised learning framework for 3D medical image segmentation. The method enhances accuracy by using task-level regularization and a dual-task approach for consistent learning.

    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

    543
    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

    8.1K

    Related Experiment Videos

    Last Updated: Oct 5, 2025

    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

    3.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    543
    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

    8.1K

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Semi-supervised learning leverages labeled and unlabeled data for improved model training.
    • Existing methods often use data or network-level consistency regularization in medical image segmentation.
    • Task-level regularization is less explored but crucial for direct accuracy improvements.

    Purpose of the Study:

    • To propose a novel semi-supervised dual-task consistent joint learning framework for 3D medical image segmentation.
    • To introduce task-level regularization to enhance segmentation accuracy.
    • To effectively utilize both labeled and unlabeled data for robust model training.

    Main Methods:

    • A dual-branch framework simultaneously predicts segmentation and signed distance maps.
    • A consistency loss function is constructed between the two tasks for joint learning.
    • Task-level regularization is applied to enforce consistency between segmentation and signed distance map prediction.

    Main Results:

    • The proposed method demonstrates superior performance on two benchmark datasets compared to state-of-the-art approaches.
    • The dual-task framework effectively utilizes unlabeled data for improved segmentation.
    • Task-level regularization contributes to enhanced geometric structure constraints and segmentation accuracy.

    Conclusions:

    • The developed semi-supervised dual-task framework offers a significant advancement in 3D medical image segmentation.
    • Task-level regularization is a viable strategy for improving segmentation accuracy in semi-supervised learning.
    • The method effectively integrates unlabeled data to boost performance and model robustness.