SWPNet: shifted windows parallel network for polyp segmentation

Qian Wang; Jiali Guo; Yu Li

doi:10.1117/12.2671366

28 April 2023 SWPNet: shifted windows parallel network for polyp segmentation

Qian Wang, Jiali Guo, Yu Li

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126103Z (2023) https://doi.org/10.1117/12.2671366
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Colorectal cancer is one of the most common malignancies that can develop from high-risk colon polyps. Currently, the application of deep learning to colon polyp segmentation has obtained high accuracy, which can have good detection rate for small polyps, but due to the limitation to the morphological diversity of polyps and unclear boundary between polyps and surrounding mucosa, there will be insufficient accuracy and unclear segmentation of marginal regions. To address the above problems, we propose Shifted Windows Parallel Network (SWPNet) for real-time accurate polyp segmentation. We design a four-layer Swin-Transformer module with window segmentation as the backbone network, which have strong feature extraction capability, and on top of the improved version of U-Net structured network with additional encoders and decoders, we add Channel Feature Pyramid (CFP) blocks on the deepest three layers of feature extraction, set different rates of dilated convolution respectively, and fuse more scales of semantic information to refine edge segmentation. We conducted experiments on each of the five classical polyp segmentation datasets and achieved high accuracy, especially on the challenging ETIS dataset with 74.1% mean Dice and 65.6% mean IoU respectively.

Citation Download Citation

Qian Wang, Jiali Guo, and Yu Li "SWPNet: shifted windows parallel network for polyp segmentation", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126103Z (28 April 2023); https://doi.org/10.1117/12.2671366

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KEYWORDS

Polyps

Image segmentation

Feature extraction

Convolution

Semantics

Network architectures

Colon

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