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

Associations between diet quality and multiple chemical exposures.

Ecotoxicology and environmental safety·2026
Same author

Shaping of microbial community structure and function by effluent-derived dissolved organic matter in a receiving river across seasons.

Eco-Environment & Health·2026
Same author

Microbial Perspective: Regulatory Mechanisms of Interactions Between Microplastics and Dissolved Organic Matter on Greenhouse Gas Emissions in Aquatic Ecosystems.

Global change biology·2026
Same author

Corrigendum to 'Exploring nonlinear responses of hydrodynamics and algal biomass to restoration measures in a eutrophic shallow urban lake' [Journal of Contaminant Hydrology 281 (2026), 104986].

Journal of contaminant hydrology·2026
Same author

Paternal Glufosinate Ammonium Exposure Leads to Memory Dysfunction in Offspring Mice.

Toxics·2026
Same author

SDMCC: Sample-wise Debiased Multilevel Contrastive Clustering for Single-cell Gene Expression Data.

IEEE journal of biomedical and health informatics·2026
Same journal

An EEG-Based Framework for Sleep Quality Assessment and Modulation with Conditional Convolutional Diffusion Modeling.

IEEE journal of biomedical and health informatics·2026
Same journal

Substantia Nigra Imaging Biomarker Segmentation for Parkinson's Disease Diagnosis via Transformer-Enhanced U-Net Architecture.

IEEE journal of biomedical and health informatics·2026
Same journal

E-TIME: Emotion Trend Inspired Multi-task Sparse Mask Neural Network for Multimodal Emotion Recognition.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross-Modal Feature Adapter for Few-Shot Human Activity Recognition.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross Domain Self-Prompting SAM2 for Intraoperative OCT Video Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Multi-Property Optimization of Antimicrobial Peptides Using Reinforcement Learning and Conditional Independence Regularization.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Jun 29, 2025

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function
11:35

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Published on: December 8, 2010

16.6K

Dual-Channel Prototype Network for Few-Shot Pathology Image Classification.

Hao Quan, Xinjia Li, Dayu Hu

    IEEE Journal of Biomedical and Health Informatics
    |April 8, 2024
    PubMed
    Summary
    This summary is machine-generated.

    A new dual-channel prototype network (DCPN) effectively classifies pathology images using few-shot learning, outperforming existing methods for rare disease diagnosis with limited data.

    More Related Videos

    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
    11:38

    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

    Published on: October 4, 2024

    548
    Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples
    08:18

    Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

    Published on: April 7, 2023

    1.6K

    Related Experiment Videos

    Last Updated: Jun 29, 2025

    Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function
    11:35

    Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

    Published on: December 8, 2010

    16.6K
    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
    11:38

    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

    Published on: October 4, 2024

    548
    Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples
    08:18

    Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

    Published on: April 7, 2023

    1.6K

    Area of Science:

    • Computational Pathology
    • Medical Image Analysis
    • Artificial Intelligence in Diagnostics

    Background:

    • Limited high-quality datasets due to rare diseases and annotation challenges impede deep learning in pathology.
    • Few-shot learning shows promise for data-scarce scenarios but is underexplored in pathology image classification.
    • Existing methods struggle with the generalizability and precision required for complex pathological feature extraction.

    Purpose of the Study:

    • To introduce a novel dual-channel prototype network (DCPN) for efficient few-shot classification of pathology images.
    • To enhance the generalizability of prototype representations by extracting multi-scale, high-precision pathological features.
    • To improve the discriminative power of models in complex pathology image classification tasks, especially for rare diseases.

    Main Methods:

    • Developed a dual-channel prototype network (DCPN) integrating self-supervised learning with a Pyramid Vision Transformer (PVT) and a Convolutional Neural Network (CNN).
    • Utilized a soft voting classifier based on multi-scale features to boost model performance.
    • Evaluated the DCPN on three public datasets (CRCTP, NCTCRC, LC25000) using few-shot classification tasks with varying domain shifts.

    Main Results:

    • The DCPN significantly outperformed the prototypical network across all few-shot learning metrics (1-shot, 5-shot, 10-shot).
    • Achieved highest accuracies in same-domain tasks: 70.86% (1-shot), 82.57% (5-shot), and 85.2% (10-shot), improving upon the prototypical network by up to 6.81%.
    • In the 10-shot setting, DCPN's accuracy (85.2%) surpassed a PVT-based supervised model (85.15%), demonstrating its potential for rare disease diagnosis.

    Conclusions:

    • The DCPN is a highly effective few-shot learning approach for pathology image classification, particularly valuable when data is limited.
    • The dual-channel architecture and multi-scale feature extraction enhance model generalizability and classification accuracy.
    • The DCPN shows significant promise for advancing deep learning-assisted diagnostics in pathology, including the identification of rare diseases.