%0 Journal Article
%T MFFNet：基于多尺度特征有效融合的息肉分割网络
MFFNet: Multi-Scale Feature Effective Fusion Network for Polyp Segmentation
%A 杨虹霞
%A 祝汉灿
%J Pure Mathematics
%P 198-210
%@ 2160-7605
%D 2025
%I Hans Publishing
%R 10.12677/pm.2025.151023
%X 息肉的准确分割对结直肠癌的治疗具有重要意义。虽然现有的方法已经取得了良好的分割效果，但仍然存在一些挑战。为此，我们提出了一个新的多尺度特征有效融合网络(MFFNet)，用于精确分割息肉。具体来说，考虑到息肉的尺寸差异，我们使用改进的Pvt-v2作为编码器(TC编码器，TC encoder)，提取丰富的多尺度特征。然后，应用通道–空间模块(Channel Spatial Module, CSM)来抑制背景信息，防止信息的冗余。为了使多尺度特征进行有效融合，我们提出了融合注意力模块(Fusion Attention Block, FAB)，该模块充分学习多层次特征之间的上下文相关性，以进一步精确定位息肉区域。在5个公共数据集上的实验表明，我们的MFFNet比其他方法具有更好的学习和泛化能力。
Accurate segmentation of polyps is important in the management of colorectal cancer. Although existing methods have achieved good segmentation results, there are still some challenges. To this end, we propose a new Multi-Scale Feature Effective Fusion Network (MFFNet) for accurate polyp segmentation. Specifically, considering the size difference of polyps, we use the improved Pvt-v2 as an encoder (TC encoder) to extract rich multi-scale features. Then, the Channel-Spatial Module (CSM) is applied to minimize background interference and prevent the redundancy of information. To enable effective fusion of multi-scale features, we propose the Fusion Attention Block (FAB), which fully learns the contextual correlations between multi-level features to further pinpoint the polyp region. Experiments on five public datasets show that our MFFNet has better learning and generalization capabilities than other methods.
%K 息肉分割，
%K 多尺度特征，
%K 特征融合，
%K 注意力机制
Polyp Segmentation
%K Multi-Scale Feature
%K Feature Fusion
%K Attention Mechanism
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=106260