HSSAM-Net: hyper-scale shifted aggregation network for precise colorectal polyp segmentation in endoscopic images.

ABSTRACT: Colorectal cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the importance of early detection through accurate polyp identification. However, colonoscopy relies heavily on precise polyp segmentation in endoscopic images, yet this task remains challenging due to morphological variability, low contrast, and imaging artifacts. In this study, we propose HSSAM-Net, a lightweight deep learning framework that integrates a Hyper-Scale Shifted Aggregation Module to capture multi-scale contextual information while preserving fine-grained details, Progressive Reuse Attention mechanism that strengthens feature propagation across the encoder-decoder pathway, and Max-Diagonal Pooling/Unpooling (MaxDP/MaxDUP) a novel dual-branch sampling scheme to improve texture represen

SUBMITTER: Feng Q 

PROVIDER: S-EPMC12578911 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature