  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 (e.g., appearance changes, small objects,
rotation) than existing representative methods. The proposed model is available
at https://github.com/DengPingFan/Polyp-PVT.
