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