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2024 | OriginalPaper | Buchkapitel

Dual-Domain Learning Network for Polyp Segmentation

verfasst von : Yan Li, Zhuoran Zheng, Wenqi Ren, Yunfeng Nie, Jingang Zhang, Xiuyi Jia

Erschienen in: Digital Forensics and Watermarking

Verlag: Springer Nature Singapore

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Abstract

Automatic polyp segmentation is a crucial application of artificial intelligence in the medical field. However, this task is challenging due to uneven brightness, variable colors, and blurry boundaries. Most current polyp segmentation methods focus on features extracted from the spatial domain, ignoring the valuable information contained in the frequency domain. In this paper, we propose a Dual-Domain Learning Network (D\(^{2}\)LNet) for polyp segmentation. Specifically, we propose a Phase-Amplitude Attention Module, which enhances the details in the phase spectrum, while reducing interference from brightness and color in the amplitude spectrum. Moreover, we introduce a Spatial-Frequency Fusion Module that utilizes parameterized frequency-domain features to adjust the style of spatial-domain features and improve polyp visibility. Extensive experiments demonstrate that our method outperforms the state-of-the-art approaches both visually and quantitatively.

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Titel: Dual-Domain Learning Network for Polyp Segmentation
verfasst von: Yan Li
Zhuoran Zheng
Wenqi Ren
Yunfeng Nie
Jingang Zhang
Xiuyi Jia
Verlag: Springer Nature Singapore
Buch: Digital Forensics and Watermarking
Print ISBN: 978-981-9725-84-7

Electronic ISBN: 978-981-9725-85-4

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2585-4_17

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