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Frequency-based boundary-guided attention network for domain generalizable polyp segmentation from colonoscopy

Ju-Hyeon Nam¹, Sang-Chul Lee¹

¹Inha University, 100, Inha-ro Incheon, 222112, Michuhol-gu, Republic of Korea.

Artificial Intelligence in Medicine

|November 14, 2025

Summary

This study introduces FBGANet, a novel network for accurate polyp segmentation in colonoscopy images. It improves polyp detection in diverse clinical settings by preserving boundary details, enhancing diagnostic accuracy.

Keywords:

Deep learning Frequency domain Medical computer vision Polyp image segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate polyp segmentation in colonoscopy is vital for colorectal cancer prevention and diagnosis.
Challenges include poor boundary details due to reflections, complex shapes, and low contrast, hindering performance in unobserved clinical settings.

Purpose of the Study:

To develop a domain-generalizable Frequency-based Boundary-guided Attention Network (FBGANet) for robust polyp segmentation.
To enhance polyp detection accuracy across various clinical domains, including unseen ones.

Main Methods:

Proposed FBGANet utilizes a DCT-based component decomposition module (DCT CDM) to remove noise from out-domain data.
Incorporated a Boundary-guided Attention Block (BGA Block) to preserve high-frequency boundary details for precise segmentation.

Main Results:

FBGANet demonstrated superior domain generalization compared to state-of-the-art methods.
Achieved higher Dice Score Coefficient (DSC) and mIoU on both in-domain and out-domain datasets.
Maintained a reasonable inference speed of 0.035 s/image.

Conclusions:

FBGANet enables precise polyp detection and segmentation in colonoscopy images, even in challenging, unobserved clinical settings.
The model's domain generalizability improves diagnostic confidence and has the potential to advance robotic-assisted healthcare and patient outcomes.