Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

TL;DR

Polyp-PVT introduces a transformer-based model with specialized modules for improved polyp segmentation, outperforming CNN-based methods in robustness and accuracy across multiple datasets.

Contribution

The paper proposes a novel transformer encoder architecture with three modules for enhanced feature fusion in polyp segmentation, addressing limitations of CNN-based approaches.

Findings

Outperforms existing methods on five datasets

Robust to appearance changes, small objects, and rotation

Effectively fuses cross-level features for better segmentation

Abstract

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.