Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

By B. Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huan Fu, Ling Shao. CAAI Artificial Intelligence Research, 2021

571 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/66b92a2250d9b899c03c3f2699a40e18e56bcd51

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In Computer Vision and Pattern Recognition

Citations in the corpus

Medical Image Segmentation via Cascaded Attention Decoding (2023, methodology): “Medical image segmentation is a dense prediction task that classifies the pixels of organs or lesions in a given medical image (e.g., CT, MRI, endoscopy, OCT, etc.) [3, 8].”
EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation (2024, methodology): “Attention mechanisms [12, 17, 20, 41, 57] have also been integrated into these models to enhance feature maps and improve pixel-level classification.”