Dual-Domain Learning Network for Polyp Segmentation

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.