# Enhancing Polyp Segmentation via Encoder Attention and Dynamic Kernel Update

arXiv: https://arxiv.org/abs/2509.23502 · HTML: https://mlsanity.com/paper/2509.23502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Adaptive Context Selection for Polyp Segmentation](https://mlsanity.com/paper/2301.04799/) (2023)
- [Lesion-aware Dynamic Kernel for Polyp Segmentation](https://mlsanity.com/paper/2301.04904/) (2023)
- [Automated polyp detection in colon capsule endoscopy](https://mlsanity.com/paper/1305.1912/) (2013)

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

- [NetworkFF: Unified Layer Optimization in Forward-Only Neural Networks](https://mlsanity.com/paper/2512.17531/) (2025)
- [Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning](https://mlsanity.com/paper/2605.18804/) (2026)

## Abstract (arXiv)

Polyp segmentation is a critical step in colorectal cancer detection, yet it remains challenging due to the diverse shapes, sizes, and low contrast boundaries of polyps in medical imaging. In this work, we propose a novel framework that improves segmentation accuracy and efficiency by integrating a Dynamic Kernel (DK) mechanism with a global Encoder Attention module. The DK mechanism, initialized by a global context vector from the EA module, iteratively refines segmentation predictions across decoding stages, enabling the model to focus on and accurately delineate complex polyp boundaries. The EA module enhances the network's ability to capture critical lesion features by aggregating multi scale information from all encoder layers. In addition, we employ Unified Channel Adaptation (UCA) in the decoder to standardize feature dimensions across stages, ensuring consistent and computationally efficient information fusion. Our approach extends the lesion-aware kernel framework by introducing a more flexible, attention driven kernel initialization and a unified decoder design. Extensive experiments on the KvasirSEG and CVC ClinicDB benchmark datasets demonstrate that our model outperforms several state of the art segmentation methods, achieving superior Dice and Intersection over Union scores. Moreover, UCA simplifies the decoder structure, reducing computational cost without compromising accuracy. Overall, the proposed method provides a robust and adaptable solution for polyp segmentation, with promising applications in clinical and automated diagnostic systems.