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

Computer-Aided Rational Hapten Design for Broad-Spectrum Monoclonal Antibody Development against Anthraquinones and Its Application in Lateral Flow Immunoassay.

Analytical chemistry·2026
Same author

Age influences serum immune indices and gut microbiota composition in adult broilers.

Frontiers in microbiology·2026
Same author

RAPT: Retrieval-Augmented Visual Prompting with Text-Guidance for Pathological Image Classification.

IEEE journal of biomedical and health informatics·2026
Same author

The benefit and risk of adding PD-1/PD-L1 inhibitors plus anti-VEGF drugs to transarterial chemoembolisation for unresectable, non-metastatic hepatocellular carcinoma: a pooled analysis of four RCTs.

Frontiers in medicine·2026
Same author

Metabolomics combined with machine learning reveals candidate metabolites and a putative working network associated with cereulide production in emetic Bacillus cereus.

Food research international (Ottawa, Ont.)·2026
Same author

Coordinated liver-serum-adipose lipid alterations and hepatic proteins regulate tissue-specific deposition of intramuscular and abdominal fat in chickens.

Poultry science·2026
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: Mar 27, 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

3.7K

Rethinking Feature Interactions for Medical Image Segmentation: A Unified Hierarchical Aggregation Framework with

Chunlin Yu, Yinhao Li, Jiaxun Li

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

    We developed Hierarchical Feature Interaction network with Boundary guidance (HFIBNet) for medical image segmentation. HFIBNet improves segmentation accuracy by enhancing feature fusion and boundary modeling.

    More Related Videos

    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
    06:18

    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

    Published on: April 5, 2024

    1.7K
    Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
    09:36

    Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

    Published on: March 14, 2018

    9.9K

    Related Experiment Videos

    Last Updated: Mar 27, 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

    3.7K
    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
    06:18

    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

    Published on: April 5, 2024

    1.7K
    Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
    09:36

    Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

    Published on: March 14, 2018

    9.9K

    Area of Science:

    • Medical image analysis
    • Computer vision
    • Deep learning for medical imaging

    Background:

    • Medical image segmentation is vital but challenging due to complex semantic information.
    • Existing encoder-decoder networks struggle with cross-scale interaction and boundary detail.

    Purpose of the Study:

    • To propose the Hierarchical Feature Interaction network with Boundary guidance (HFIBNet).
    • To enhance medical image segmentation by improving feature fusion and boundary modeling.

    Main Methods:

    • Introduced a Boundary Prediction (BP) module for boundary-aware features.
    • Designed Cross-Level Feature Fusion (CLFF) and Edge Feature Aggregation (EFA) modules for hierarchical feature interaction.
    • Utilized a Partially Parallel Decoder (PPD) and Global-Local Feature Enrichment (GLFE) module for coarse-to-fine segmentation.

    Main Results:

    • HFIBNet demonstrated superior performance across ten public medical segmentation datasets.
    • The method consistently outperformed existing state-of-the-art segmentation techniques.
    • Achieved significant improvements in segmentation accuracy and structural consistency.

    Conclusions:

    • HFIBNet offers an effective solution for medical image segmentation.
    • The proposed architecture successfully unifies dynamic feature fusion and explicit edge supervision.
    • HFIBNet shows great potential for clinical applications in medical image analysis.