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 12, 2026

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

A Dual-branch Network with Cross-scale Feature Interaction and Alignment for Weakly Supervised Whole Slide Image

Jianan Zhang, Hangbei Cheng, Xueyu Liu

    IEEE Journal of Biomedical and Health Informatics
    |June 10, 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

    The safety and efficacy of human umbilical cord mesenchymal stem cell for acute respiratory distress syndrome: an open-label and multicenter phase 1 clinical trial.

    Frontiers in immunology·2026
    Same author

    [Targeted inhibition of miR-503 upregulates Apelin expression to alleviate myocardial infarction in mice].

    Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026
    Same author

    Effective Gaussian Management for High-fidelity Scene Reconstruction.

    IEEE transactions on visualization and computer graphics·2026
    Same author

    SMC4 is a hypoxia-responsive driver of pulmonary arterial hypertension through an HIF-1α-HDAC6-p21 regulatory axis.

    Respiratory research·2026
    Same author

    Decouple, Collaborate, Match: Prototype-Driven Cognitive Mutual Learning for Brain Tumor Segmentation.

    IEEE journal of biomedical and health informatics·2026
    Same author

    Inhibition of the QPCT-PDIA4 axis rescues ΔF508 and N1303K CFTR in cystic fibrosis.

    Nature communications·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

    This study introduces FIA-MIL, a novel dual-branch network for whole slide images (WSIs) analysis. The method enhances computer-aided diagnosis by effectively capturing hierarchical information and dependencies in WSIs using cross-scale feature interaction and alignment.

    Area of Science:

    • Computational pathology
    • Digital pathology
    • Medical image analysis

    Background:

    • Whole slide images (WSIs) analysis is crucial for computer-aided diagnosis.
    • Weakly supervised multiple instance learning (MIL) is widely used for WSI processing due to the lack of pixel-level annotations.
    • Existing MIL methods often fail to fully utilize the pyramidal structure of WSIs and capture inter-instance dependencies and local context.

    Purpose of the Study:

    • To propose FIA-MIL, a dual-branch network for weakly supervised WSI analysis.
    • To address limitations in capturing hierarchical information and inter-instance dependencies in current MIL approaches.
    • To improve the performance of WSI analysis by incorporating cross-scale feature interaction and alignment.

    Main Methods:

    More Related Videos

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
    10:30

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

    Published on: October 15, 2014

    Multimodal Approach to Assess Bone Regeneration and Scaffold Performance
    06:54

    Multimodal Approach to Assess Bone Regeneration and Scaffold Performance

    Published on: February 13, 2026

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Automatic Identification of Dendritic Branches and their Orientation
    06:08

    Automatic Identification of Dendritic Branches and their Orientation

    Published on: September 17, 2021

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
    10:30

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

    Published on: October 15, 2014

    Multimodal Approach to Assess Bone Regeneration and Scaffold Performance
    06:54

    Multimodal Approach to Assess Bone Regeneration and Scaffold Performance

    Published on: February 13, 2026

  • Developed FIA-MIL, a dual-branch network integrating cross-scale feature interaction and alignment.
  • Employed a dual-scale feature interaction module with Transformer encoders to model semantic relationships across magnifications and capture instance-level dependencies.
  • Utilized a dual-scale feature aggregation module with alignment constraints for multi-scale feature integration and semantic consistency.

    • Main Results:

    • The proposed FIA-MIL method demonstrated promising performance in classification and survival analysis tasks.
    • Experiments were conducted on publicly available whole slide images datasets.
    • The method effectively leverages the pyramidal structure and captures hierarchical information within WSIs.

    Conclusions:

    • FIA-MIL offers an effective approach for weakly supervised whole slide images analysis.
    • The dual-branch network architecture with cross-scale feature interaction and alignment improves the capture of hierarchical information and dependencies.
    • The proposed method shows potential for advancing computer-aided diagnosis in digital pathology.