Computer Science > Computer Vision and Pattern Recognition
[Submitted on 6 Jul 2021 (v1), last revised 22 Jul 2021 (this version, v3)]
Title:UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation
View PDFAbstract: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 this https URL
Submission history
From: Taehun Kim [view email][v1] Tue, 6 Jul 2021 03:11:12 UTC (2,790 KB)
[v2] Mon, 12 Jul 2021 12:26:27 UTC (2,451 KB)
[v3] Thu, 22 Jul 2021 00:20:25 UTC (2,453 KB)
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