  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.
