  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.
