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

A data-informed multidimensional composite score for stress assessment.

Acta psychologica·2026
Same author

Biosensing of Steroid Hormones with 3D Zinc Oxide Tetrapods.

ACS omega·2026
Same author

An Ensemble of Long Short-Term Memory Models to Automatically Detect End-Range Movement Patterns in Men's Professional Hard Court Grand Slam Tennis.

European journal of sport science·2026
Same author

Redefining roles in wheelchair basketball: A data driven approach to characterising playing positions.

Journal of sports sciences·2026
Same author

Multi-resolution and Multi-modal Feature Integration using Graph Neural Networks for Optical Coherence Tomography Image Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Robust ECG Classification using Mamba and Self-Supervised Representation Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Anatomically and biochemically guided deep image prior for sodium MRI denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Segment Anything Model for medical image segmentation: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
See all related articles

Related Experiment Video

Updated: Oct 8, 2025

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

555

A survey on graph-based deep learning for computational histopathology.

David Ahmedt-Aristizabal1, Mohammad Ali Armin2, Simon Denman3

  • 1Imaging and Computer Vision Group, CSIRO Data61, Canberra, Australia; SAIVT, Queensland University of Technology, Brisbane, Australia.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 27, 2021
PubMed
Summary
This summary is machine-generated.

Graph deep learning enhances digital pathology by analyzing tissue structure beyond image patches. This approach captures complex cellular interactions for improved tumor diagnosis and prediction, offering new research avenues.

Keywords:
Cancer classificationCell-graphDeep learningDigital pathologyGraph Convolutional NetworksHierarchical graph representationTissue-graph

More Related Videos

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
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.2K

Related Experiment Videos

Last Updated: Oct 8, 2025

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

555
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
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.2K

Area of Science:

  • Computational pathology
  • Medical imaging analysis
  • Graph neural networks

Background:

  • Machine learning and deep learning are increasingly used in digital pathology.
  • Convolutional neural networks analyzing image patches struggle to capture global tissue context.
  • Histological entity distribution is crucial for accurate tissue diagnosis.

Purpose of the Study:

  • To provide a conceptual overview of graph analytics in digital pathology.
  • To review the application of graph deep learning in various diagnostic tasks.
  • To identify limitations and future research directions in the field.

Main Methods:

  • Reviewing literature on graph data representations and deep learning for pathology.
  • Organizing methods by graph representation, scale, and organ.
  • Discussing entity-graph construction and graph architectures.

Main Results:

  • Graph analytics show success in tumor localization, classification, invasion staging, image retrieval, and survival prediction.
  • These methods effectively model tissue composition and inter-entity interactions.
  • The review systematically categorizes current graph-based approaches.

Conclusions:

  • Graph deep learning offers a powerful framework for analyzing complex tissue structures in digital pathology.
  • This approach overcomes limitations of patch-wise analysis by incorporating global context.
  • Further research is needed to refine techniques and expand applications in cancer diagnostics.