Dynamic Contextual Attention Network: Transforming Spatial Representations into Adaptive Insights for Endoscopic Polyp Diagnosis
This addresses polyp diagnosis for colorectal cancer screening, with incremental improvements in contextual modeling.
The paper tackles the problem of inaccurate polyp localization and lack of contextual awareness in endoscopic imaging for colorectal cancer detection, proposing the Dynamic Contextual Attention Network (DCAN) which improves diagnostic performance and interpretability.
Colorectal polyps are key indicators for early detection of colorectal cancer. However, traditional endoscopic imaging often struggles with accurate polyp localization and lacks comprehensive contextual awareness, which can limit the explainability of diagnoses. To address these issues, we propose the Dynamic Contextual Attention Network (DCAN). This novel approach transforms spatial representations into adaptive contextual insights, using an attention mechanism that enhances focus on critical polyp regions without explicit localization modules. By integrating contextual awareness into the classification process, DCAN improves decision interpretability and overall diagnostic performance. This advancement in imaging could lead to more reliable colorectal cancer detection, enabling better patient outcomes.