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Related Experiment Video

Updated: May 10, 2026

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

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

Published on: May 24, 2022

An Edge-Enhanced Network for Polyp Segmentation.

Yao Tong^1,2, Ziqi Chen³, Zuojian Zhou^1,2

¹School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China.

Bioengineering (Basel, Switzerland)

|October 25, 2024

Summary

This summary is machine-generated.

This study introduces EENet, an advanced AI model for accurate polyp segmentation in colonoscopy images. EENet significantly improves early detection of colorectal cancer by precisely identifying polyps, aiding in timely intervention.

Keywords:

attention mechanism convolutional neural network edge enhancement polyp segmentation

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Colorectal cancer is a major global health concern, with early polyp detection crucial for prevention.
Automated polyp segmentation in colonoscopy images is challenging due to visual variability and low contrast.

Purpose of the Study:

To develop an enhanced network (EENet) for accurate automated polyp segmentation.
To improve the precision of polyp detection in colonoscopy for better colorectal cancer screening.

Main Methods:

Proposed EENet integrating covariance edge-enhanced attention (CEEA) and cross-scale edge enhancement (CSEE) modules.
Utilized a hybrid loss function combining cross-entropy and edge-aware loss.
Evaluated performance on Kvasir-SEG and CVC-ClinicDB datasets.

Main Results:

EENet achieved a Dice score of 0.9208 and IoU of 0.8664 on Kvasir-SEG.
Achieved a Dice score of 0.9316 and IoU of 0.8817 on CVC-ClinicDB.
Outperformed state-of-the-art models like Polyp-PVT and PraNet.

Conclusions:

The proposed EENet demonstrates superior performance in polyp segmentation.
The CEEA and CSEE modules effectively enhance edge detection and feature preservation.
EENet shows significant potential for clinical application in colorectal cancer screening.