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

Expression of tissue factor pathway inhibitor-2 in gastric stromal tumor and its clinical significance.

Experimental and therapeutic medicine·2014
Same author

Facile access to cytocompatible multicompartment micelles with adjustable Janus-cores from A-block-B-graft-C terpolymers prepared by combination of ROP and ATRP.

Colloids and surfaces. B, Biointerfaces·2014
Same author

Functional layers for Zn(II) ion detection: from molecular design to optical fiber sensors.

The journal of physical chemistry. B·2013
Same author

Expression of the 78 kD glucose-regulated protein is induced by endoplasmic reticulum stress in the development of hepatopulmonary syndrome.

Gene·2013
Same author

Multi-nuclear silver(I) and copper(I) complexes: a novel bonding mode for bispyridylpyrrolides.

Dalton transactions (Cambridge, England : 2003)·2013
Same author

Transcriptome profilings of female Schistosoma japonicum reveal significant differential expression of genes after pairing.

Parasitology research·2013
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Jul 31, 2025

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.5K

A Structure-Aware Hierarchical Graph-Based Multiple Instance Learning Framework for pT Staging in Histopathological

Jiangbo Shi, Lufei Tang, Yang Li

    IEEE Transactions on Medical Imaging
    |May 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework for pathological primary tumor (pT) staging using whole slide images (WSIs). The structure-aware hierarchical graph-based multi-instance learning framework (SGMF) improves accuracy in cancer staging by analyzing multiple magnifications.

    More Related Videos

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
    05:22

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

    Published on: June 21, 2024

    464
    Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
    08:40

    Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

    Published on: April 8, 2016

    12.9K

    Related Experiment Videos

    Last Updated: Jul 31, 2025

    Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
    11:19

    Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

    Published on: March 20, 2018

    10.5K
    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
    05:22

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

    Published on: June 21, 2024

    464
    Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
    08:40

    Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

    Published on: April 8, 2016

    12.9K

    Area of Science:

    • Digital pathology
    • Computational oncology
    • Medical image analysis

    Background:

    • Pathological primary tumor (pT) staging is crucial for cancer prognosis and treatment, relying on analyzing tumor infiltration in gigapixel whole slide images (WSIs).
    • Pixel-level annotation is challenging due to multiple magnifications, leading to pT staging being typically framed as a weakly supervised WSI classification task.
    • Current methods often use multiple instance learning (MIL) on single-magnification patches, failing to capture critical cross-scale contextual information essential for accurate pT staging.

    Purpose of the Study:

    • To develop an advanced weakly supervised learning framework for accurate pathological primary tumor (pT) staging from whole slide images (WSIs).
    • To address the limitations of existing methods in capturing multi-magnification contextual information for improved cancer staging.
    • To mimic the diagnostic process of pathologists by integrating hierarchical and structural information.

    Main Methods:

    • Proposed a structure-aware hierarchical graph (SAHG) to organize instances (patches) within WSIs, capturing spatial relationships across magnifications.
    • Developed a hierarchical attention-based graph representation (HAGR) network to learn cross-scale spatial features critical for pT staging.
    • Utilized a global attention layer to aggregate information from top nodes of the SAHG for a comprehensive slide-level representation.

    Main Results:

    • The proposed structure-aware hierarchical graph-based multi-instance learning framework (SGMF) demonstrated significant effectiveness in pT staging.
    • SGMF outperformed state-of-the-art methods by up to 5.6% in F1 score on three large-scale, multi-center datasets across two cancer types.
    • The framework successfully captured critical patterns by learning cross-scale spatial features through its hierarchical attention mechanism.

    Conclusions:

    • The novel SGMF framework effectively addresses the challenge of integrating multi-magnification information for accurate pT staging in digital pathology.
    • The structure-aware hierarchical graph and attention-based network design are key to capturing essential contextual patterns for improved cancer staging.
    • This approach offers a promising advancement in weakly supervised learning for pathological image analysis, enhancing diagnostic accuracy and potentially patient outcomes.