Clinically, automated polyp segmentation techniques have the potential to
significantly improve the efficiency and accuracy of medical diagnosis, thereby
reducing the risk of colorectal cancer in patients. Unfortunately, existing
methods suffer from two significant weaknesses that can impact the accuracy of
segmentation. Firstly, features extracted by encoders are not adequately
filtered and utilized. Secondly, semantic conflicts and information redundancy
caused by feature fusion are not attended to. To overcome these limitations, we
propose a novel approach for polyp segmentation, named MLFF-Net, which
leverages multi-level feature fusion and attention mechanisms. Specifically,
MLFF-Net comprises three modules: Multi-scale Attention Module (MAM),
High-level Feature Enhancement Module (HFEM), and Global Attention Module
(GAM). Among these, MAM is used to extract multi-scale information and polyp
details from the shallow output of the encoder. In HFEM, the deep features of
the encoders complement each other by aggregation. Meanwhile, the attention
mechanism redistributes the weight of the aggregated features, weakening the
conflicting redundant parts and highlighting the information useful to the
task. GAM combines features from the encoder and decoder features, as well as
computes global dependencies to prevent receptive field locality. Experimental
results on five public datasets show that the proposed method not only can
segment multiple types of polyps but also has advantages over current
state-of-the-art methods in both accuracy and generalization ability.

为了提高医学诊断的效率和准确度，减少患者结直肠癌的风险，我们提出了一种名为 MLFF-Net 的新型息肉分割方法，该方法利用多级特征融合和注意机制。实验证明该方法不仅可以分割多种类型的息肉，还在准确性和概括能力方面优于现有最先进的方法。