## 2 Related Works
### Principal Models in Deep Learning Polyp Segmentation  
The significant strides in deep learning algorithms have led to the proposal of numerous models for polyp segmentation, including CNN-based approaches, transformer-based approaches, and hybrid-network-based approaches. Recent methods [29; 30; 31; 32; 33] have adopted the CNN architecture, enabling end-to-end polyp representation and segmentation. The inception of Fully Convolutional Networks (FCNs) by [PERSON] et al. [10] initiated a crucial evolution towards models amenable to end-to-end training in the realm of segmentation. This was further advanced by [PERSON] et al. [11] through the development of the UNet architecture, which was specifically created for biomedical image segmentation. This architecture combines deep and shallow image features using a symmetrical U-shaped encoder-decoder structure, along with skip connections. The FCN and UNet became the foundational frameworks for medical image segmentation, with subsequent methods [34; 35; 36; 37] building largely upon them. [PERSON] et al. [12] \begin{table}
\begin{tabular}{p{85.4 pt}|p{113.8 pt}|p{113.8 pt}} \hline \hline
**Related Studies** & **Methods** & **Typical Works** & **Key Characteristics** \\ \hline Principal model architectures & \begin{tabular}{c} CNN-based methods \\ \end{tabular} & \begin{tabular}{c} [10; 11; 12], etc. \\ \end{tabular} & **Pros:** Strong local feature extraction capabilities. \\ \begin{tabular}{c} **Cons:** Limited ability to capture global dependencies, which may lead to loss of feature information. \\ **Pros:** Excellent global modeling capabilities, able to capture long-range dependencies. \\ **Cons:** High computational complexity, longer training times. \\ **Pros:** Combines the strengths of CNNs and transformers, capturing both local features and global dependencies. \\ **Cons:** Complex structure, challenging to design effective fusion modules. \\ **Innovative hybrid architecture that combines transformer, CNN, and MLP, which captures both local features and global dependencies and enhances the feature fusion workflow.** \\ \hline Semi-supervised learning & \begin{tabular}{c} Pseudo-label-based methods \\ \end{tabular} & \
\begin{tabular}{c} [17; 18; 19], etc. \\ \end{tabular} & **Pros:** Effective even with limited labeled data, iterative refinement of predictions. \\
\begin{tabular}{c} **Cons:** Initial pseudo-label quality is crucial; poor labels can degrade performance. \\ **Pros:** Ensures model consistency, effective use of unlabeled data. \\ **Cons:** Requires multiple forward passes, increasing computational demands. \\ **Pros:** Captures complex patterns, improves generalization across domains. \\ **Cons:** High training complexity, stability issues, and significant computational resources required. \\ **Pros:** Learns robust feature representations, effective with large amounts of unlabeled data. \\ **Cons:** Requires careful selection of contrastive pairs; balancing contrastive loss can be complex. \\ **Innovative semi-supervised framework combining CPS with patch-wise contrastive learning, enhancing feature representation and segmentation accuracy. \\ \end{tabular}
\end{table} 
Table 1: Overview of related works. have developed a method for polyp segmentation utilizing the FCN. This method includes an innovative approach to image patch selection during training and an efficient post-processing technique applied to the probability map during testing, significantly improving segmentation accuracy in colonoscopy images. [PERSON] et al. [38] have introduced the Enhanced U-Net model, which incorporates a semantic feature enhancement module and an adaptive global context module to progressively refine feature quality, demonstrating superior performance in polyp segmentation tasks. Additionally, [PERSON] and [PERSON] [39] have developed the dual U-Net model with a ResNet Encoder, leveraging the strengths of both U-Net and ResNet architectures [40] to enhance polyp segmentation accuracy.