Traditional segmentation methods for colonic polyps are mainly designed based
on low-level features. They could not accurately extract the location of small
colonic polyps. Although the existing deep learning methods can improve the
segmentation accuracy, their effects are still unsatisfied. To meet the above
challenges, we propose a hybrid network called Fusion-Transformer-HardNetMSEG
(i.e., Fu-TransHNet) in this study. Fu-TransHNet uses deep learning of
different mechanisms to fuse each other and is enhanced with multi-view
collaborative learning techniques. Firstly, the Fu-TransHNet utilizes the
Transformer branch and the CNN branch to realize the global feature learning
and local feature learning, respectively. Secondly, a fusion module is designed
to integrate the features from two branches. The fusion module consists of two
parts: 1) the Global-Local Feature Fusion (GLFF) part and 2) the Dense Fusion
of Multi-scale features (DFM) part. The former is built to compensate the
feature information mission from two branches at the same scale; the latter is
constructed to enhance the feature representation. Thirdly, the above two
branches and fusion modules utilize multi-view cooperative learning techniques
to obtain their respective weights that denote their importance and then make a
final decision comprehensively. Experimental results showed that the
Fu-TransHNet network was superior to the existing methods on five widely used
benchmark datasets. In particular, on the ETIS-LaribPolypDB dataset containing
many small-target colonic polyps, the mDice obtained by Fu-TransHNet were 12.4%
and 6.2% higher than the state-of-the-art methods HardNet-MSEG and TransFuse-s,
respectively.

本研究提出一种名为 Fusion-Transformer-HardNetMSEG (Fu-TransHNet) 的混合网络，通过多种深度学习机制融合和多视角协作学习技术提高结肠息肉的分割精度。富 - TransHNet 利用变压器和 CNN 分支实现全局和局部特征学习，设计了一个融合模块来整合两个分支的特征，利用多视图协作学习技术获取各自权重，结果表明 Fu-TransHNet 网络在五个广泛使用的基准数据集上优于现有方法。