Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (2108.06932v8)

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.

• The paper presents the Polyp-PVT model leveraging a Pyramid Vision Transformer encoder to robustly extract multiscale features.
• A cascaded fusion and camouflage identification modules effectively aggregate semantic and low-level details, enhancing segmentation precision.
• The model outperforms state-of-the-art methods, achieving up to a 5.5% improvement in mean Dice on benchmark polyp segmentation datasets.

Polyp-PVT: Transformer-Driven Polyp Segmentation

The paper "Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers" by Bo Dong et al. describes a novel framework for polyp segmentation using a Pyramid Vision Transformer (PVT) as an encoder, diverging from the traditional CNN-based methods. This approach aims to address the challenges posed by polyp segmentation, such as differentiating contribution levels of features and effectively fusing these features in medical image analysis. Unlike conventional CNNs, the transformer-based method offers more robust feature representation and improved model generalization.

Key Contributions

The paper introduces the Polyp-PVT model, which integrates several innovative modules to enhance the polyp segmentation process:

1. Pyramid Vision Transformer (PVT) Encoder: The authors utilize a pyramid architecture similar to CNNs but enhance its performance using spatial-reduction attention operations. The transformer encoder demonstrates improved robustness against image noise and better feature extraction capabilities.
2. Cascaded Fusion Module (CFM): This module processes high-level features to aggregate semantic and locational information progressively. The CFM ensures effective integration of high-level feature maps, balancing accuracy and computational efficiency.
3. Camouflage Identification Module (CIM): The CIM focuses on extracting polyp details, such as texture and edges from low-level features, which often camouflage with background tissues. It employs channel and spatial attention mechanisms to enhance valuable cues and suppress irrelevant data.
4. Similarity Aggregation Module (SAM): Featuring a graph convolutional network, the SAM effectively combines high- and low-level features, enriching the feature maps with global context and pixel-level details through self-attention mechanisms.

Experimental Evaluation

The Polyp-PVT model was evaluated on five benchmark datasets: Kvasir-SEG, ClinicDB, ColonDB, Endoscene, and ETIS. It outperformed several existing state-of-the-art methods, including PraNet, SANet, and MSEG, particularly in handling challenging conditions like appearance changes, small object detection, and rotation. For instance, on ColonDB, Polyp-PVT achieved a mean Dice of 0.808, outperforming the next best method, SANet, by 5.5%. It also showed significant improvements in generalization to unseen data across multicentric datasets.

Implications and Future Work

The paper highlights the potential of breaking away from traditional CNN architectures by leveraging transformers for medical image segmentation tasks. The findings suggest that transformers, with their inherent design, provide more robust feature representations, especially useful in varied medical imaging scenarios where noise and camouflage-like conditions exist.

Future developments could explore further optimization of Polyp-PVT for real-time applications and investigate the adaptability of vision transformers in other domains of medical image analysis. Moreover, addressing current limitations, such as accurately handling reflective points, could enhance the framework's robustness.

Overall, this paper offers valuable insights into the benefits of adopting transformer-based architectures for complex segmentation tasks and suggests a promising direction for future research in medical imaging.