SemiPolypSeg: Leveraging Cross-Pseudo Supervision and Contrastive Learning for Semi-Supervised Polyp Segmentation

Abstract

The colonoscopy is the foremost technique for detecting polyps, where accurate segmentation is crucial for effective diagnosis and surgical preparation. Nevertheless, contemporary deep learning-based methods for polyp segmentation face substantial hurdles due to the large amount of labeled data required. To address this, we introduce an innovative semi-supervised learning framework based on cross-pseudo supervision (CPS) and contrastive learning, termed Semi-supervised Polyp Segmentation (SemiPolypSeg), which requires only limited labeled data. First, a new segmentation architecture, the Hybrid Transformer–CNN Segmentation Network (HTCSNet), is proposed to enhance semantic representation and segmentation performance. HTCSNet features a parallel encoder combining transformers and convolutional neural networks, as well as an All-MLP decoder with skip connections to streamline feature fusion and enhance decoding efficiency. Next, the integration of CPS in SemiPolypSeg enforces output consistency across diverse perturbed datasets and models, guided by the consistency loss principle. Finally, patch-wise contrastive loss discerns feature disparities between positive and negative sample pairs as delineated by the projector. Comprehensive evaluation demonstrated our method’s superiority over existing state-of-the-art semi-supervised segmentation algorithms. Specifically, our method achieved Dice Similarity Coefficients (DSCs) of 89.68% and 90.62% on the Kvasir-SEG dataset with 15% and 30% labeled data, respectively, and 89.72% and 90.06% on the CVC-ClinicDB dataset with equivalent ratios.