# DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation

arXiv: https://arxiv.org/abs/2012.15245 · HTML: https://mlsanity.com/paper/2012.15245/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 30 of 31)

- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [FCN-Transformer Feature Fusion for Polyp Segmentation](https://mlsanity.com/paper/2208.08352/) (2022)
- [ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2205.08473/) (2022)
- [TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2206.08985/) (2022)
- [TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation](https://mlsanity.com/paper/2207.03450/) (2022)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data](https://mlsanity.com/paper/2406.00016/) (2024)
- [BlazeNeo: Blazing fast polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2203.00129/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2105.00402/) (2021)
- [DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation](https://mlsanity.com/paper/2210.13595/) (2022)
- [FCB-SwinV2 Transformer for Polyp Segmentation](https://mlsanity.com/paper/2302.01027/) (2023)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention](https://mlsanity.com/paper/2506.18335/) (2025)
- [Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy](https://mlsanity.com/paper/2107.02319/) (2021)
- [Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges](https://mlsanity.com/paper/2307.16262/) (2023)
- [Know Your Attention Maps: Class-specific Token Masking for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2507.06848/) (2025)
- [Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation](https://mlsanity.com/paper/2301.06673/) (2023)
- [Cooperation Learning Enhanced Colonic Polyp Segmentation Based on Transformer-CNN Fusion](https://mlsanity.com/paper/2301.06892/) (2023)
- [UGCANet: A Unified Global Context-Aware Transformer-based Network with Feature Alignment for Endoscopic Image Analysis](https://mlsanity.com/paper/2307.06260/) (2023)
- [RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation](https://mlsanity.com/paper/2307.06420/) (2023)
- [SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation](https://mlsanity.com/paper/2406.14819/) (2024)
- [PSTNet: Enhanced Polyp Segmentation with Multi-scale Alignment and Frequency Domain Integration](https://mlsanity.com/paper/2409.08501/) (2024)
- [Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation](https://mlsanity.com/paper/2104.00850/) (2021)
- [Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network](https://mlsanity.com/paper/2206.06264/) (2022)
- [A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n](https://mlsanity.com/paper/2507.10864/) (2025)
- [Synthetic Data-Driven Multi-Architecture Framework for Automated Polyp Segmentation Through Integrated Detection and Mask Generation](https://mlsanity.com/paper/2508.06170/) (2025)
- [KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation](https://mlsanity.com/paper/2312.08555/) (2023)
- [Hybrid(Transformer+CNN)-based Polyp Segmentation](https://mlsanity.com/paper/2508.09189/) (2025)

## Abstract (arXiv)

Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions), an automated, accurate, and fast segmentation can improve the quality of delineated lesion boundaries and reduce missed rate. The Endotect challenge provides an opportunity to benchmark computer vision methods by training on the publicly available Hyperkvasir and testing on a separate unseen dataset. In this paper, we propose a novel architecture called ``DDANet'' based on a dual decoder attention network. Our experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577, demonstrating the generalization ability of our model.