UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation
This work addresses polyp segmentation in medical imaging, providing a novel method that significantly improves accuracy for healthcare applications, though it is incremental as it builds on existing U-Net architectures.
The paper tackles polyp segmentation by proposing UACANet, which uses uncertainty-augmented context attention to handle uncertain areas in saliency maps, achieving state-of-the-art performance with a 76.6% mean Dice on the ETIS dataset, a 13.8% improvement over previous methods.
We propose Uncertainty Augmented Context Attention network (UACANet) for polyp segmentation which consider a uncertain area of the saliency map. We construct a modified version of U-Net shape network with additional encoder and decoder and compute a saliency map in each bottom-up stream prediction module and propagate to the next prediction module. In each prediction module, previously predicted saliency map is utilized to compute foreground, background and uncertain area map and we aggregate the feature map with three area maps for each representation. Then we compute the relation between each representation and each pixel in the feature map. We conduct experiments on five popular polyp segmentation benchmarks, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300, and achieve state-of-the-art performance. Especially, we achieve 76.6% mean Dice on ETIS dataset which is 13.8% improvement compared to the previous state-of-the-art method. Source code is publicly available at https://github.com/plemeri/UACANet