DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
This work aims to improve the accuracy and speed of polyp segmentation for clinicians, reducing human error and missed lesions in colonoscopy.
The paper addresses the challenge of automatic polyp segmentation in colonoscopy images, which is crucial for treatment and prognosis. The proposed DDANet model, trained on Kvasir-SEG and tested on an unseen dataset, achieved a Dice coefficient of 0.7874 and an mIoU of 0.7010.
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.