Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Dec 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Ensemble Feature Network for Gastric Section Classification.

Ting-Hsuan Lin, Jyun-Yao Jhang, Chun-Rong Huang

IEEE Journal of Biomedical and Health Informatics

|August 6, 2020

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

Pretreatment feeding‑stoma placement in advanced esophageal cancer: defining optimal patient selection criteria.

Esophagus : official journal of the Japan Esophageal Society·2026

Same author

ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model.

IEEE open journal of engineering in medicine and biology·2026

Same author

Using Class and Domain Information to Address Domain Shift in Federated Learning.

IEEE transactions on neural networks and learning systems·2026

Same author

Gastric Cancer Prevention in Taiwan: Past Achievements and Future Perspectives.

Helicobacter·2026

Same author

Screening and eradication of <i>Helicobacter pylori f</i>or gastric cancer prevention: Taipei Global Consensus II.

Gut·2025

Same author

NLSTseg: A Pixel-level Lung Cancer Dataset Based on NLST LDCT Images.

Scientific data·2025

Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026

Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026

Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026

Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026

Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026

Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026

See all related articles

We developed a new deep ensemble feature (DEF) network for classifying gastric endoscopic images. This method simultaneously learns features and classifiers, outperforming existing deep learning and ensemble techniques.

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Gastric section classification from endoscopic images is crucial for diagnosis.
Current deep ensemble learning methods often require separate training of features and classifiers.
This limits the efficiency and performance of fused classification results.

Purpose of the Study:

To propose a novel deep ensemble feature (DEF) network for end-to-end classification of gastric sections.
To enable simultaneous learning of deep ensemble features and decision classifiers.
To improve classification accuracy compared to existing methods.

Main Methods:

The proposed DEF network consists of two sub-networks: an ensemble feature network and a decision network.
The ensemble feature network learns a unified feature representation from multiple Convolutional Neural Networks (CNNs).

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Dec 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

The decision network utilizes these features for classification, optimized via ensemble feature and decision losses.

Main Results:

The DEF network achieved superior performance in classifying gastric sections.
Experimental results demonstrated that the proposed method outperforms state-of-the-art deep learning, ensemble learning, and deep ensemble learning approaches.
The end-to-end trainable nature allows for simultaneous optimization of feature extraction and classification.

Conclusions:

The novel DEF network offers an effective and efficient approach for gastric endoscopic image classification.
Simultaneous learning of features and classifiers in an end-to-end manner leads to improved performance.
This method represents a significant advancement in deep ensemble learning for medical image analysis.