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

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

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Same author

MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.

Research square·2026

Same author

Segmentation regularized training for multi-domain deep learning registration applied to magnetic resonance-guided prostate cancer radiotherapy.

Physics and imaging in radiation oncology·2026

Same author

Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images.

Proceedings of machine learning research·2026

Same author

AsyCMST: Asymmetric cross-modal spatio-temporal learning for multimodal ultrasound nodule recognition.

Medical image analysis·2026

Same author

CD8+ T cells in Hashimoto's thyroiditis-associated papillary thyroid carcinoma.

European thyroid journal·2026

Same journal

Radiomics-based causal machine learning for exploratory treatment-effect estimation of neoadjuvant chemotherapy cycle intensity in osteosarcoma: a proof-of-concept study.

BMC medical imaging·2026

Same journal

Gestational age-specific MRI reference values for fetal renal morphology and ADC.

BMC medical imaging·2026

Same journal

MRI findings of intrahepatic cholangiocarcinoma with sarcomatoid differentiation: a retrospective case series.

BMC medical imaging·2026

Same journal

Multimodal deep learning for papillary thyroid carcinoma diagnosis using ultrasound and cytology.

BMC medical imaging·2026

Same journal

MonoGID: geometry and illumination aware enhancement with distillation for self-supervised monocular endoscopic depth estimation.

BMC medical imaging·2026

Same journal

Application of transformer attention mechanism-based multimodal deep learning model in the diagnosis of papillary thyroid carcinoma.

BMC medical imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

DCATNet: polyp segmentation with deformable convolution and contextual-aware attention network.

Zenan Wang¹, Tianshu Li¹, Ming Liu²

¹Department of Gastroenterology, Beijing Chaoyang Hospital, The Third Clinical Medical College of Capital Medical University, Beijing, China.

BMC Medical Imaging

|April 14, 2025

Summary

This summary is machine-generated.

DCATNet, a novel deep learning model, significantly improves polyp segmentation accuracy in medical images. This advancement aids computer-aided diagnosis by overcoming challenges in polyp size, shape, and texture variability.

Keywords:

Colorectal Polyp Deep learning Deformable attention Transformer polyp segmentation

More Related Videos

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

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

Related Experiment Videos

Last Updated: May 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

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

Area of Science:

Medical Imaging
Artificial Intelligence
Computer-Aided Diagnosis

Background:

Polyp segmentation is vital for computer-aided diagnosis but is hindered by complex medical images and anatomical variations.
Existing methods struggle with segmenting polyps due to inconsistent size, shape, and texture, leading to inaccurate results.

Purpose of the Study:

To introduce DCATNet, a novel deep learning architecture for precise polyp segmentation.
To address the limitations of current state-of-the-art methods in handling polyp variability.

Main Methods:

DCATNet employs a U-shaped network integrating ResNetV2-50 for local features and a Transformer for long-range dependencies.
Key components include the Geometry Attention Module (GAM), Contextual Attention Gate (CAG), and Multi-scale Feature Extraction (MSFE) block.

Main Results:

DCATNet achieved high mean dice scores (0.9351 on Kvasir-SEG, 0.9444 on CVC-ClinicDB), surpassing previous state-of-the-art methods.
Cross-validation confirmed DCATNet's strong generalization capabilities.
Ablation studies validated the contribution of each module (GAM, CAG, MSFE) to improved segmentation.

Conclusions:

DCATNet demonstrates superior performance in polyp segmentation, offering precise and reliable results.
The integrated modules effectively enhance feature representation and fusion.
DCATNet shows significant potential for clinical applications in medical image segmentation.