ADNet: Anisotropic Deformable Network for Enhanced Boundary-Aware Polyp Segmentation

Automated polyp segmentation in colonoscopy images plays an essential role in the early detection and prevention of colorectal cancer, one of the leading causes of cancer-related deaths worldwide. Recent deep learning approaches have achieved remarkable segmentation accuracy, but require significant computational resources and a large number of parameters. To support real-time clinical decision making while lowering the computational burden, we thus propose Anisotropic Deformable Network (ADNet), a lightweight polyp segmentation framework incorporating three key principles in specifically designed modules: (1) deformable strip convolutions, (2) differential feature extraction and (3) cross-scale kernel attention mechanism. The first two components are integrated into each encoder layer, while the last is introduced into decoder. These modules support effective feature extraction and multi-scale interaction while maintaining low computational complexity. Extensive experiments demonstrate the competitive segmentation performance and inference time of ADNet compared to state-of-the-art approaches, while reducing memory consumption, making it suitable for efficient real-time clinical deployment.