## 1 Introduction  
Colorectal Cancer (CRC), the third most prevalent form of cancer worldwide, accounted for an estimated 1.9 million new cases and over 930,000 deaths in 2020 alone. By 2040, the burden of CRC is projected to increase to 3.2 million new cases and 1.6 million deaths annually, underscoring the urgent need for effective early diagnosis and intervention [1]. Given that the 5-year relative survival rate for localized CRC is about 90%, early detection and intervention are critical in improving patient outcomes and reducing mortality rates [2]. In this context, the detection and removal of colonic polyps during colonoscopy play a pivotal role in preventing CRC progression. Polyp segmentation, the process of delineating polyps from surrounding tissue in colonoscopic images, is a key task for enhancing the effectiveness of automated polyp detection systems [3]. These systems are designed to assist endoscopists by identifying potentially precancerous lesions, thereby improving diagnostic accuracy and reducing the risk of oversight. Despite its importance, automated polyp segmentation is fraught with challenges. Traditional approaches to polyp segmentation have largely relied on supervised learning techniques, which depend on extensive annotated datasets. These datasets require precise,pixel-level labels, which are both time-consuming and costly to produce due to the need for expert medical knowledge. Given this constraint, several unsupervised learning techniques have been proposed [4; 5]. However, these methods lack the accuracy needed for effective polyp image segmentation. Instead, the semi-supervised learning (SSL) approach offers a promising alternative. By utilizing both labeled and unlabeled data, semi-supervised methods can potentially reduce the dependency on large annotated datasets, thus mitigating the bottleneck of manual annotation, while obtaining high segmentation accuracy. Two new SSL methods, cross-pseudo supervision (CPS) [6] and contrastive learning [7; 8], have been proposed to enhance semantic segmentation performance. Although CPS and contrastive learning have shown promise in SSL for semantic segmentation in natural environments, further investigation is needed to explore their combined application specifically for polyp segmentation. Moreover, SSL networks for polyp segmentation are predominantly based on either convolutional operations or transformer architectures [9]. Convolutional neural networks (CNNs) excel at extracting local features through their hierarchical structure, which allows them to capture intricate patterns and textures with great precision. However, they inherently lack the ability to grasp global context, as their localized receptive fields focus on small regions without considering the entire image. On the other hand, transformers, known for their self-attention mechanisms, are adept at modeling global dependencies, allowing them to grasp the overall context and relationships across the whole input image. Despite this, transformers often fall short in capturing the fine-grained details that CNNs handle so well, leading to a potential compromise in the quality of localized feature representation. This dichotomy between the two methodologies poses a challenge in achieving optimal polyp segmentation, necessitating a fusion of detailed local insights with an overarching global perspective. To tackle the above challenges, our paper presents a novel semi-supervised segmentation framework for polyp segmentation, named **SemiPolypSeg**, which leverages both labeled and unlabeled data through CPS and contrastive learning. Furthermore, we introduce a new polyp segmentation network architecture that integrates transformer and CNN architectures called the Hybrid Transformer-CNN Segmentation Network (**HTCSNet**), which is aimed at improving the performance of SSL in polyp segmentation. Our newly developed framework surpasses other SSL approaches, delivering superior accuracy with minimal labeled data.