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: Dec 21, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Unsupervised Tumor Characterization via Conditional Generative Adversarial Networks.

Quoc Dang Vu, Kyungeun Kim, Jin Tae Kwak

    IEEE Journal of Biomedical and Health Informatics
    |May 13, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Cancer-Critical Genes II: Tumor Suppressor Genes01:05

    Cancer-Critical Genes II: Tumor Suppressor Genes

    9.3K
    Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
    When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
    Such genes that act...
    9.3K

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Tumor-Associated Macrophage Infiltration and PD-L1 Expression in Gastric Cancer According to a Modified TCGA-Based Classification.

    Journal of gastric cancer·2026
    Same author

    Cell strain-stiffening drives cell breakout from embedded spheroids.

    ArXiv·2026
    Same author

    Multimodal Approach for Identification and Validation of Hepatocellular Carcinoma Targets for Radiotheranostics.

    bioRxiv : the preprint server for biology·2025
    Same author

    Evaluating the robustness of slide-level AI predictions on out-of-focus whole slide images: A retrospective observational study.

    Journal of pathology informatics·2025
    Same author

    Correction: Colorectal cancers with a residual adenoma component: Clinicopathologic features and KRAS mutation.

    PloS one·2025
    Same author

    A robust artificial intelligence system for predicting EBV status in gastric cancer biopsy and resection specimens.

    Scientific reports·2025
    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 a novel quantitative method for cancer grading using conditional generative adversarial networks. The approach offers an unsupervised way to characterize tissues, potentially improving accuracy in digital pathology.

    Area of Science:

    • Digital pathology
    • Computational pathology
    • Artificial intelligence in oncology

    Background:

    • Cancer grading, based on differentiation, is crucial for treatment but relies on subjective microscopic assessment.
    • Pathologist variability in cancer grading leads to inconsistencies in diagnosis and treatment.
    • Digital pathology aims to automate and improve cancer grading accuracy, but often mimics human assessment.

    Purpose of the Study:

    • To develop an unsupervised, quantitative method for cancer characterization and grading.
    • To move beyond mimicking human pathologists towards objective tissue assessment.
    • To leverage generative adversarial networks for novel cancer pathology insights.

    Main Methods:

    • Utilized conditional generative adversarial networks (cGANs) for unsupervised tissue characterization.

    Related Experiment Videos

    Last Updated: Dec 21, 2025

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.6K
  • Applied the method to whole slide images (WSIs) and tissue microarrays (TMAs).
  • Evaluated on colorectal cancer (CRC) specimens.

    • Main Results:

    • The proposed method provides a quantitative approach to assess cancer characteristics.
    • Demonstrated potential for characterizing tissues in an unsupervised manner.
    • Showcased applicability on real-world colorectal cancer data.

    Conclusions:

    • The developed method offers a quantitative alternative for cancer assessment, moving beyond subjective grading.
    • Conditional generative adversarial networks show promise for unsupervised cancer characterization in digital pathology.
    • This approach has the potential to enhance the accuracy and robustness of cancer pathology analysis.