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: Sep 24, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Dense Convolutional Network and Its Application in Medical Image Analysis.

Tao Zhou^1,2, XinYu Ye^1,2, HuiLing Lu³

¹School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China.

Biomed Research International

|May 5, 2022

Summary

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

Mitigating structural recalcitrance: Microwave pretreatment governs anatomy-dependent moisture migration and dimensional stability of moso bamboo.

Bioresource technology·2026

Same author

Hyperspectral geographical origin identification of Bupleurum via spatial-spectral features based on adaptive weighted quaternion zernike moments.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

Same author

Spatial atlas of human diabetic kidney uncovered podocyte-driven metabolic-inflammatory crosstalk via glycerolipid reprogramming and DUSP4/MMP3 axis.

Fundamental research·2026

Same author

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Journal of proteomics·2026

Same author

Efficacy and safety of BCMA- or GPRC5D-directed CD3 bispecific antibodies in relapsed/refractory multiple myeloma: a systematic review and meta-analysis of prospective clinical trials and real-world studies.

Frontiers in immunology·2026

Same author

KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes.

Briefings in bioinformatics·2026

Same journal

Resveratrol Mitigates Noise-Induced Cochlear Damage and Delays Hearing Loss in Wistar Rats.

BioMed research international·2026

Same journal

RETRACTION: Green Fabrication of Silver Nanoparticles Using Euphorbia Serpens Kunth Aqueous Extract, their Characterization, and Investigation of its in Vitro Antioxidative, Antimicrobial, Insecticidal, and Cytotoxic Activities.

BioMed research international·2026

Same journal

Predictors of Prolonged Hospital Length of Stay in Patients With Odontogenic Infections in Ghana.

BioMed research international·2026

Same journal

Traditional Chinese Medicine Bone-Setting Techniques Research Progress for the Treatment of Knee Osteoarthritis.

BioMed research international·2026

Same journal

RETRACTION: miR-375 Inhibits the Proliferation and Invasion of Nasopharyngeal Carcinoma Cells by Suppressing PDK1.

BioMed research international·2026

Same journal

Exploring the Therapeutic Potential of Nobiletin in Nonsmall Cell Lung Cancer.

BioMed research international·2026

See all related articles

This summary is machine-generated.

This paper reviews Dense convolutional networks (DenseNet), a deep learning model with significant medical image analysis applications. It details DenseNet

Area of Science:

Deep Learning
Computer Vision
Medical Imaging

Background:

Dense convolutional network (DenseNet) is a prominent deep learning architecture.
DenseNet has demonstrated significant utility in medical image analysis tasks.

Purpose of the Study:

To systematically review the fundamental principles of DenseNet.
To analyze the evolution of DenseNet architectures, including structural variations, lightweight designs, dense units, connection modes, and attention mechanisms.
To summarize DenseNet applications in medical image analysis, focusing on pattern recognition, image segmentation, and object detection.

Main Methods:

Review of DenseNet principles and architectural developments.
Analysis of five key areas: broadened structure, lightweight structure, dense unit, dense connection mode, and attention mechanism.

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Sep 24, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Categorization of applications into pattern recognition, image segmentation, and object detection.

Main Results:

A comprehensive overview of DenseNet's core concepts.
Detailed analysis of architectural advancements and modifications.
Systematic summary of its diverse applications in medical imaging.

Conclusions:

The systematic summarization of DenseNet architectures and applications provides valuable insights.
This review is significant for advancing research and development in DenseNet for medical image analysis.