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

Visual System01:26

Visual System

1.4K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Engineering G-quadruplex aptamer to modulate its binding specificity.

National science review·2021
Same author

Minimally Invasive Delivery of 3D Shape Recoverable Constructs with Ordered Structures for Tissue Repair.

ACS biomaterials science & engineering·2021
Same author

Lenvatinib in combination with transarterial chemoembolization for treatment of unresectable hepatocellular carcinoma (uHCC): a retrospective controlled study.

Hepatology international·2021
Same author

Loss of microRNA-147 function alleviates synovial inflammation through ZNF148 in rheumatoid and experimental arthritis.

European journal of immunology·2021
Same author

Dependence of SARS-CoV-2 infection on cholesterol-rich lipid raft and endosomal acidification.

Computational and structural biotechnology journal·2021
Same author

Palladium-Catalyzed Remote C-H Phosphonylation of Indoles at the C4 and C6 Positions by a Radical Approach.

Angewandte Chemie (International ed. in English)·2021

Related Experiment Video

Updated: Nov 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.7K

Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN.

Fengping An1, Xiaowei Li1, Xingmin Ma2

  • 1School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China.

Oxidative Medicine and Cellular Longevity
|March 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning approach for medical image classification, combining visual attention mechanisms and multiscale convolutional neural networks. The method enhances accuracy and robustness for tasks like lung nodule and breast cancer detection.

More Related Videos

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.9K

Related Experiment Videos

Last Updated: Nov 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.7K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.9K

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Deep Learning Applications

Background:

  • Traditional medical image classification struggles with complexity.
  • Deep learning offers potential but faces challenges in model construction and adaptability for medical data.

Purpose of the Study:

  • To develop an improved deep learning model for medical image classification.
  • To enhance feature extraction, model interpretability, and training strategies for medical images.

Main Methods:

  • Integration of a visual attention mechanism for finer granularity information extraction and improved interpretability.
  • Development of a novel multiscale convolutional neural network (CNN) for automatic high-level feature extraction.
  • Utilization of Mahalanobis distance optimization for enhanced training strategies and model robustness.

Main Results:

  • The proposed visual attention-based multiscale CNN achieved superior accuracy in classifying lung nodules and breast cancer images compared to traditional methods.
  • The model demonstrated improved performance over existing deep learning techniques.
  • The method exhibited good stability and robustness in medical image classification tasks.

Conclusions:

  • The developed algorithm effectively addresses limitations in current deep learning models for medical image classification.
  • The combination of visual attention and multiscale CNNs offers a promising direction for accurate and robust medical image analysis.