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

TL;DR

CL-Polyp introduces a contrastive learning-based network with lightweight modules that significantly improves polyp segmentation accuracy across multiple benchmark datasets without requiring additional annotations.

Contribution

The paper proposes a novel contrastive learning-enhanced architecture with efficient modules, improving feature extraction and boundary reconstruction in polyp segmentation tasks.

Findings

Outperforms state-of-the-art methods on five benchmark datasets.

Achieves higher IoU scores, e.g., +0.011 on Kvasir-SEG.

Enhances boundary reconstruction and multi-scale feature fusion.

Abstract

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 like classification to improve segmentation. However, these methods often need more labeled data and depend on task similarity, potentially limiting generalizability. To address these challenges, we propose CL-Polyp, a contrastive learning-enhanced polyp segmentation network. Our method uses contrastive learning to enhance the encoder's extraction of discriminative features by contrasting positive and negative sample pairs from polyp images. This self-supervised strategy improves visual representation without needing additional annotations. We also introduce two efficient, lightweight modules: the Modified Atrous Spatial Pyramid Pooling (MASPP) module for improved multi-scale feature fusion, and the Channel Concatenate and Element Add (CA) module to merge low-level and upsampled features for {enhanced} boundary reconstruction. Extensive experiments on five benchmark datasets-Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS-show that CL-Polyp consistently surpasses state-of-the-art methods. Specifically, it enhances the IoU metric by 0.011 and 0.020 on the Kvasir-SEG and CVC-ClinicDB datasets, respectively, demonstrating its effectiveness in clinical polyp segmentation.