MDPNet: Multiscale Dynamic Polyp-focus Network for Enhancing Medical Image Polyp Segmentation

Colorectal cancer (CRC) is the most common malignant neoplasm in the digestive system and a primary cause of cancer-related mortality in the United States, exceeded only by lung and prostate cancers. The American Cancer Society estimates that in 2024, there will be approximately 152,810 new cases of colorectal cancer and 53,010 deaths in the United States, highlighting the critical need for early diagnosis and prevention. Precise polyp segmentation is crucial for early detection, as it improves treatability and survival rates. However, existing methods, such as the UNet architecture, struggle to capture long-range dependencies and manage the variability in polyp shapes and sizes, and the low contrast between polyps and the surrounding background. We propose a multiscale dynamic polyp-focus network (MDPNet) to solve these problems. It has three modules: dynamic polyp-focus (DPfocus), non-local multiscale attention pooling (NMAP), and learnable multiscale attention pooling (LMAP). DPfocus captures global pixel-to-polyp dependencies, preserving high-level semantics and emphasizing polyp-specific regions. NMAP stabilizes the model under varying polyp shapes, sizes, and contrasts by dynamically aggregating multiscale features with minimal data loss. LMAP enhances spatial representation by learning multiscale attention across different regions. This enables MDPNet to understand long-range dependencies and combine information from different levels of context, boosting the segmentation accuracy. Extensive experiments on four publicly available datasets demonstrate that MDPNet is effective and outperforms current state-of-the-art segmentation methods by 2-5% in overall accuracy across all datasets. This demonstrates that our method improves polyp segmentation accuracy, aiding early detection and treatment of colorectal cancer.