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

AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.

BMC biology·2026
Same author

TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity.

PLoS computational biology·2026
Same author

Adaptive feature unlearning for trustworthy medical imaging privacy.

Medical image analysis·2026
Same author

Deep learning-based Breast Imaging Reporting and Data System classification and establishment of diagnostic model in breast cancer diagnosis with automated breast ultrasound.

Quantitative imaging in medicine and surgery·2026
Same author

Anatomy-Guided Spatiotemporal Affinity Learning for Unsupervised Domain Adaptation in Echocardiography Segmentation.

IEEE journal of biomedical and health informatics·2026
Same author

Performance Evaluation of SmartBrain: A Wearable PET System for Human Brain Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026

Related Experiment Video

Updated: Jul 14, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.9K

ColonNet: A novel polyp segmentation framework based on LK-RFB and GPPD.

Dong Sui1, Weifeng Liu1, Yue Zhang2

  • 1School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.

Computers in Biology and Medicine
|October 7, 2023
PubMed
Summary

This study introduces ColonNet, a fast and efficient AI framework for segmenting colorectal polyps during colonoscopies. ColonNet achieves high accuracy and significantly improves processing speed, addressing key limitations in current diagnostic tools.

Keywords:
ConvNeXtGlobal Parallel Partial DecoderLarge-Kernel Receptive Field BlockPolyp segmentation

More Related Videos

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

439
Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids
10:23

Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids

Published on: May 3, 2024

914

Related Experiment Videos

Last Updated: Jul 14, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

439
Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids
10:23

Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids

Published on: May 3, 2024

914

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Colorectal cancer (CRC) is a leading cause of cancer death globally.
  • Current CRC screening relies heavily on colonoscopy, with polyp diagnosis being crucial.
  • Existing AI polyp segmentation methods struggle with accuracy, generalization, and speed.

Purpose of the Study:

  • To develop a fast and efficient AI framework for accurate polyp segmentation in colonoscopy images.
  • To overcome the limitations of current segmentation methods in terms of speed and accuracy.

Main Methods:

  • Proposed ColonNet framework utilizing Large-Kernel Receptive Field Block (LK-RFB) and Global Parallel Partial Decoder (GPPD).
  • Extensive testing on multiple public datasets, including CVC-300.

Main Results:

  • ColonNet achieved a DICE coefficient exceeding 0.910 and an FPS over 102 on the CVC-300 dataset.
  • Outperformed or matched state-of-the-art methods on five public datasets.
  • Demonstrated superior processing speed (FPS > 102) while maintaining high segmentation accuracy.

Conclusions:

  • ColonNet represents a significant advancement in AI-powered polyp segmentation for colorectal cancer screening.
  • The framework offers a promising solution for faster, more accurate, and generalized polyp detection in clinical settings.