CL-Polyp: A Contrastive Learning-Enhanced Network for Accurate Polyp Segmentation

Accurate segmentation of polyps from colonoscopy images is crucial for the early diagnosis and treatment of colorectal cancer. Most existing deep learning-based polyp segmentation methods adopt an Encoder-Decoder architecture, and some utilize multi-task frameworks that incorporate auxiliary tasks such as classification to enhance segmentation performance. However, these approaches often require additional labeled data and rely on task similarity, which can limit their generalizability. To address these challenges, we propose CL-Polyp, a contrastive learning-enhanced polyp segmentation network. Our method leverages contrastive learning to improve the encoder's ability to extract discriminative features by contrasting positive and negative sample pairs derived from polyp images. This self-supervised strategy enhances visual representation without requiring additional annotations. In addition, we introduce two lightweight and effective modules: the Modified Atrous Spatial Pyramid Pooling (MASPP) module for better multi-scale feature fusion, and the Channel Concatenate and Element Add (CA) module to fuse low-level and upsampled features for improved boundary reconstruction. Extensive experiments on five benchmark datasets-Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS-demonstrate that CL-Polyp consistently outperforms state-of-the-art methods. Specifically, it improves the IoU metric by 0.011 and 0.020 on the Kvasir-SEG and CVC-ClinicDB datasets, respectively, validating its effectiveness in clinical polyp segmentation tasks.