# Dynamic Contextual Attention Network: Transforming Spatial Representations into Adaptive Insights for Endoscopic Polyp Diagnosis

arXiv: https://arxiv.org/abs/2504.20306 · HTML: https://mlsanity.com/paper/2504.20306/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Rotate to Attend: Convolutional Triplet Attention Module](https://mlsanity.com/paper/2010.03045/) (2020)
- [Global Context Networks](https://mlsanity.com/paper/2012.13375/) (2020)
- [Guided Context Gating: Learning to leverage salient lesions in retinal fundus images](https://mlsanity.com/paper/2406.13126/) (2024)
- [A Deep Convolutional Neural Network for the Detection of Polyps in Colonoscopy Images](https://mlsanity.com/paper/2008.06721/) (2020)

## Built on by (most cited first, 2 of 2)

- [Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging](https://mlsanity.com/paper/2604.17222/) (2026)
- [C2A: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/2608.09774/) (2026)

## Abstract (arXiv)

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.