Bilateral collaborative streams with multi-modal attention network for accurate polyp segmentation

Accurate segmentation of colorectal polyps in colonoscopy images represents a critical prerequisite for early cancer detection and prevention. However, existing segmentation approaches struggle with the inherent diversity of polyp presentations, variations in size, morphology, and texture, while maintaining the computational efficiency required for clinical deployment. To address these challenges, we propose a novel dual-stream architecture, Bilateral Convolutional Multi-Attention Network (BiCoMA). The proposed network integrates both global contextual information and local spatial details through parallel processing streams that leverage the complementary strengths of convolutional neural networks and vision transformers. The architecture employs a hybrid backbone where the convolutional stream utilizes ConvNeXt V2 Large to extract high-resolution spatial features, while the transformer stream employs Pyramid Vision Transformer to model global dependencies and long-range contextual relationships. Our model employs Spatial Refinement (SR) modules to process high-resolution convolutional features and, thereby preserving critical boundary information through asymmetric convolutional processing. Channel Refinement (CR) and Non-Local Attention (NLA) mechanisms are integrated into transformer features and to enhance discriminative capacity and capture global contextual relationships. These refined multi-scale representations from both streams are progressively integrated through a hierarchical decoder incorporating a Pyramidal Attention Block (PAB) for multi-scale feature processing and Convolutional Block Attention Modules (CBAM) for enhanced feature discrimination. The fusion strategy employs systematic upsampling with lateral connections, enabling adequate information flow from semantic understanding to fine-grained spatial localization. Channel alignment operations through and convolutions ensure computational efficiency while preserving essential semantic information. The proposed BiCoMA architecture achieves state-of-the-art performance across five benchmark datasets (Endoscene, ClinicDB, ColonDB, ETIS, and Kvasir-SEG), demonstrating superior generalization capabilities and practical computational requirements for real-time clinical applications.