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The aim is to enhance the accuracy of colorectal polyp segmentation in challenging endoscopic images.

Waveformer: Dual-Branch Adaptive Network with Wavelet-Guided Cross-Context Decoding for Colorectal Polyp Segmentation

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The aim is to enhance the accuracy of colorectal polyp segmentation in challenging endoscopic images.
Developed a dual-branch adaptive network for feature extraction.
Utilized wavelet-based frequency decomposition for enhanced edge responses.
Implemented camouflage identification and information fusion for detailed semantic aggregation.
Conducted experiments on CVC-ClinicDB and Kvasir-SEG datasets.
Achieved Dice Similarity Coefficients of 95.60% and 94.11% on CVC-ClinicDB and Kvasir-SEG datasets, respectively.
Outperformed fourteen state-of-the-art methods in segmentation accuracy.
Demonstrated strong generalization ability with cross-dataset evaluations yielding DSC scores of 81.0% and 79.2%.

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Demonstrates improved polyp segmentation in endoscopic images, suggesting better diagnostic accuracy in clinical settings.

LIANG et al. (2026) studied this question.

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