Most polyp segmentation methods use CNNs as their backbone, leading to two
key issues when exchanging information between the encoder and decoder: 1)
taking into account the differences in contribution between different-level
features and 2) designing an effective mechanism for fusing these features.
Unlike existing CNN-based methods, we adopt a transformer encoder, which learns
more powerful and robust representations. In addition, considering the image
acquisition influence and elusive properties of polyps, we introduce three
standard modules, including a cascaded fusion module (CFM), a camouflage
identification module (CIM), and a similarity aggregation module (SAM). Among
these, the CFM is used to collect the semantic and location information of
polyps from high-level features; the CIM is applied to capture polyp
information disguised in low-level features, and the SAM extends the pixel
features of the polyp area with high-level semantic position information to the
entire polyp area, thereby effectively fusing cross-level features. The
proposed model, named Polyp-PVT, effectively suppresses noises in the features
and significantly improves their expressive capabilities. Extensive experiments
on five widely adopted datasets show that the proposed model is more robust to
various challenging situations (\emph{e.g.}, appearance changes, small objects,
rotation) than existing representative methods. The proposed model is available
at this https URL

本文提出了基于 Transformer 编码器、三个标准模块（包括串级融合模块，伪装识别模块和相似聚合模 块）的息肉分割方法，名为 Polyp-PVT。实验结果表明，该方法在人口学多样性、外观变化，甚至缺失数据方 面都具有较好的鲁棒性。