• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0217002 (2025)
Jian Xu1,2,* and Ruohan Wang1
Author Affiliations
  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu , China
  • 2Jiangsu Key Laboratory of Big Data Security and Intelligent Processing (Nanjing University of Posts and Telecommunications), Nanjing 210023, Jiangsu , China
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    DOI: 10.3788/LOP241160 Cite this Article Set citation alerts
    Jian Xu, Ruohan Wang. Reliable Polyp Segmentation Based on Local Channel Attention[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0217002 Copy Citation Text show less
    References

    [1] Arnold M, Sierra M S, Laversanne M et al. Global patterns and trends in colorectal cancer incidence and mortality[J]. Gut, 66, 683-691(2017).

    [2] Liu J W, Liu Q H, Li X O et al. Improved colonic polyp segmentation method based on double U-shaped network[J]. Acta Optica Sinica, 41, 1810001(2021).

    [3] Tajbakhsh N, Gurudu S R, Liang J M. Automated polyp detection in colonoscopy videos using shape and context information[J]. IEEE Transactions on Medical Imaging, 35, 630-644(2016).

    [4] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. International conference on medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

    [5] Zhou Z W, Siddiquee M M R, Tajbakhsh N et al. UNet++: redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE Transactions on Medical Imaging, 39, 1856-1867(2020).

    [6] Jha D, Smedsrud P H, Riegler M A et al. ResUNet: an advanced architecture for medical image segmentation[C], 225-2255(2019).

    [7] Su Z P, Li J W, Jiang J W et al. Semantic segmentation method for remote sensing images based on improved DeepLabV3+[J]. Laser & Optoelectronics Progress, 60, 0628003(2023).

    [8] Fang Y Q, Chen C, Yuan Y X et al. Selective feature aggregation network with area-boundary constraints for polyp segmentation[M]. Medical image computing and computer-assisted intervention- MICCAI 2019, 11764, 302-310(2019).

    [9] Fan D P, Ji G P, Zhou T et al. PraNet: parallel reverse attention network for polyp segmentation[M]. Medical image computing and computer-assisted intervention- MICCAI 2020, 12266, 263-273(2020).

    [10] Wei J, Hu Y W, Zhang R M et al. Shallow attention network for polyp segmentation[M]. Medical image computing and computer-assisted intervention- MICCAI 2021, 12901, 699-708(2021).

    [11] Shan F M, Wang M W, Li M. Multi-scale convolutional neural network incorporating attention mechanism for intestinal polyp segmentation[J]. Journal of Graphics, 44, 50-58(2023).

    [12] Li S Y, Li Q, Guan X. A 3D renal tumor image segmentation method based on U2-Net[J]. Laser & Optoelectronics Progress, 60, 2210010(2023).

    [13] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth[EB/OL], 16-16. https://arxiv.org/abs/2010.11929

    [14] Yuan Y, Chen M H, Ke S T et al. Fundus image classification research based on ensemble convolutional neural network and vision transformer[J]. Chinese Journal of Lasers, 49, 2007205(2022).

    [15] Wang W H, Xie E Z, Li X et al. Pyramid vision transformer: a versatile backbone for dense prediction without convolutions[C], 568-578(2021).

    [16] Wang W H, Xie E Z, Li X et al. PVT v2: improved baselines with pyramid vision transformer[J]. Computational Visual Media, 8, 415-424(2022).

    [17] Chen J N, Lu Y Y, Yu Q H et al. TransUNet: transformers make strong encoders for medical image segmentation[EB/OL]. http://arxiv.org/abs/2102.04306v1

    [18] Dong B, Wang W H, Fan D P et al. Polyp-PVT: polyp segmentation with pyramid vision transformers[J]. CAAI Artificial Intelligence Research, 2, 9150015(2023).

    [19] Rahman M M, Marculescu R. Medical image segmentation via cascaded attention decoding[C], 6211-6220(2023).

    [20] van Amersfoort J, Smith L, Teh Y W et al. Uncertainty estimation using a single deep deterministic neural network[EB/OL]. http://arxiv.org/abs/2003.02037v2

    [21] Wang M, Lin T, Wang L Y et al. Uncertainty-inspired open set learning for retinal anomaly identification[J]. Nature Communications, 14, 6757(2023).

    [22] Oktay O, Schlemper J, le Folgoc L et al. Attention U-Net: learning where to look for the pancreas[EB/OL]. http://arxiv.org/abs/1804.03999v3

    [23] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    [24] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

    [25] Chen L, Zhang H W, Xiao J et al. SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning[C], 6298-6306(2017).

    [26] Shen Y S, Cremers D. Deep combinatorial aggregation[EB/OL]. https://arxiv.org/abs/2210.06436

    [27] Razavi A, van den Oord A, Vinyals O. Generating diverse high-fidelity images with vq-vae-2[C], 14866-14876(2019).

    [28] Dempster A P. A generalization of Bayesian inference[M]. Classic works of the dempster-shafer theory of belief functions, 219, 73-104(2008).

    [29] Sensoy M, Kaplan L, Kandemir M. Evidential deep learning to quantify classification uncertainty[C], 3183-3193(2018).

    [30] Jha D, Smedsrud P H, Riegler M A et al. Kvasir-SEG: a segmented polyp dataset[M]. Multimedia modeling, 11962, 451-462(2020).

    [31] Silva J, Histace A, Romain O et al. Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer[J]. International Journal of Computer Assisted Radiology and Surgery, 9, 283-293(2014).

    [32] Bernal J, Sánchez F J, Fernández-Esparrach G et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians[J]. Computerized Medical Imaging and Graphics, 43, 99-111(2015).

    [33] Vázquez D, Bernal J, Sánchez F J et al. A benchmark for endoluminal scene segmentation of colonoscopy images[J]. Journal of Healthcare Engineering, 2017, 4037190(2017).