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

An automatic progressive chromosome segmentation approach using deep learning with traditional image processing.

Medical & biological engineering & computing·2023
Same author

Distribution and risk of mercury in the sediments of mangroves along South China Coast.

Ecotoxicology (London, England)·2020
Same author

Improving Tabletability of Excipients by Metal-Organic Framework-Based Cocrystallization: a Study of Mannitol and CaCl<sub>2</sub>.

Pharmaceutical research·2020
Same author

Protective Effects of the King Oyster Culinary-Medicinal Mushroom, Pleurotus eryngii (Agaricomycetes), Polysaccharides on β-Amyloid-Induced Neurotoxicity in PC12 Cells and Aging Rats, In Vitro and In Vivo Studies.

International journal of medicinal mushrooms·2020
Same author

Effect of grape seed extract on quality and microbiota community of container-cultured snakehead (Channa argus) fillets during chilled storage.

Food microbiology·2020
Same author

Stable and Efficient Single-Atom Zn Catalyst for CO<sub>2</sub> Reduction to CH<sub>4</sub>.

Journal of the American Chemical Society·2020

Related Experiment Video

Updated: Oct 6, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

546

Colon tissue image segmentation with MWSI-NET.

Hao Cheng1, Kaijie Wu2, Jie Tian3

  • 1Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.

Medical & Biological Engineering & Computing
|January 19, 2022
PubMed
Summary

This study introduces a novel deep learning model for accurate colon cancer detection in pathological images. The computer-aided diagnosis system enhances accuracy and efficiency in identifying cancerous tissues.

Keywords:
Attention mechanismDilated convolutionMulti-scalePathological image segmentation

More Related Videos

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.0K

Related Experiment Videos

Last Updated: Oct 6, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

546
A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.0K

Area of Science:

  • Oncology
  • Computer Science
  • Medical Imaging

Background:

  • Manual analysis of colon tissues for cancer diagnosis is time-consuming and subjective.
  • Deep learning advancements offer potential for automated computer-aided diagnosis in oncology.
  • Pathological images present challenges due to irregularity, tissue similarity, and large size.

Purpose of the Study:

  • To develop an automated method for precise identification of cancerous regions in colon cancer pathology.
  • To overcome limitations of manual analysis and improve diagnostic accuracy and efficiency.

Main Methods:

  • A multi-scale perceptual field fusion structure utilizing a dilated convolutional network.
  • Incorporation of dilated convolution kernels with varied aspect ratios to capture diverse cancerous region sizes.
  • Integration of two attention mechanisms to emphasize cancerous areas within pathological images.

Main Results:

  • The proposed model effectively fuses detailed information across multiple scales for enhanced semantic segmentation.
  • Demonstrated improved efficacy in identifying cancerous regions compared to existing methods.
  • Validated using a large, open-source dataset.

Conclusions:

  • The developed deep learning model offers a robust solution for computer-aided diagnosis of colon cancer.
  • The multi-scale fusion and attention mechanisms significantly improve the accuracy of cancerous region identification.
  • This approach holds promise for more efficient and objective pathological analysis.