## 2 Related Works
### Semi-Supervised Medical Image Segmentation Methods
#### 2.2.4 Contrastive-Learning-Based Methods  
Contrastive learning [60] has become well-regarded for its capability to learn robust feature representations from limited labeled data. By contrasting positive pairs against negative pairs, models can encode meaningful representations beneficial for segmentation tasks. This method is particularly effective for datasets with large amounts of unlabeled data but requires the careful selection of pairs to ensure diversity and representativeness. Contrastive-learning-based methods are increasingly recognized for enhancing medical image segmentation accuracy by leveraging unlabeled data to strengthen feature representation robustness and reduce the need for labeled data. [PERSON] et al. [26] introduced a semi-supervised contrastive learning approach for label-efficient segmentation. However, it faces difficulties with complex images where contrastive pairs may not fully capture dataset diversity, potentially leading to suboptimal feature representations. [PERSON] et al. [27] unveiled multiscale cross-contrastive learning, which is adept at discerning hierarchical structures within medical images. Despite its advantages, balancing contrastive loss across multiple scales increases complexity and could be computationally intensive. [PERSON] et al. [28] integrated contrastive learning with shape awareness to refine anatomical structure segmentation. This innovative approach requires careful calibration to maintain shape awareness without compromising the core principles of contrastive learning.  
Upon careful consideration, it becomes clear that the four cornerstone methodologies of semi-supervised medical image segmentation--PLBMs, CRBMs, GBMs, and CLBMs--have each significantly advanced the field. Yet, despite their notable contributions, they are not devoid of intrinsic limitations. The introduction of a novel semi-supervised framework that adeptly merges these methods heralds a promising research trajectory and a practical resolution.  
CPS can be viewed as an excellent method combining the strengths of PLBMs and CRBMs. It employs two structurally identical networks, which are differently initialized and constrained by cross-entropy loss to produce similar outputs for the same input. Eachnetwork generates pseudo-labels for input samples, which are used as supervisory signals for the other network. This encourages similar predictions and expands training data with pseudo-labels from unlabeled data.  
Despite its innovation, CPS falls short in harnessing the full potential of unlabeled data, as it prioritizes maintaining consistency rather than uncovering new data patterns. To address this, integrating contrastive learning could be transformative. Contrastive learning excels in distinguishing between similar and dissimilar data points, fostering more robust feature representations that are critical for model reliability. This enhancement not only promises more precise pseudo-labels but also a deeper exploration of untapped patterns within unlabeled data. In our study, we innovatively explore the use of patch-wise contrastive learning [61] within the CPS framework to enhance the effectiveness of semi-supervised models in polyp segmentation.