[1] Bray F, Ferlay J, Soerjomataram I et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 68, 394-424(2018).

[2] Chen W Q, Sun K X, Zheng R S et al. Cancer incidence and mortality in China, 2014[J]. Chinese Journal of Cancer Research, 30, 1-12(2018).

[3] Wu C X, Gu K, Gong Y M et al. Analysis of incidence and mortality of colorectal cancer in China, 2015[J]. China Oncology, 30, 241-245(2020).

[4] Leufkens A M, Vleggaar F P et al. Factors influencing the miss rate of polyps in a back-to-back colonoscopy study[J]. Endoscopy, 44, 470-475(2012).

[5] Zhang H L, Li Q, Guan X. An improved three-dimensional dual-path brain tumor image segmentation network[J]. Acta Optica Sinica, 41, 0310002(2021).

[6] Huang H, Peng C, Wu R Y et al. Self-supervised transfer learning of pulmonary nodule classification based on partially annotated CT images[J]. Acta Optica Sinica, 40, 1810003(2020).

[7] Li D X, Zhang Z. Improved U-net segmentation algorithm for the retinal blood vessel images[J]. Acta Optica Sinica, 40, 1010001(2020).

[8] Wang Y G, Xi Y Y, Pan X Y. Method for intestinal polyp segmentation by improving DeepLabv3+ network[J]. Journal of Frontiers of Computer Science and Technology, 14, 1243-1250(2020).

[9] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. ∥Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science. Cham: Springer, 9351, 234-241(2015).

[10] [10] Wang LS, Chen RZ, Hu YX. IDDF2018-ABS-0261 Polyp detection using an unet based model[C]∥International Digestive Disease Forum (IDDF) 2018, June 9-10, 2018, Hong Kong, China. London: BMJ Publishing Group Limited, 2018, 67: A85.

[11] Zhou Z W. Siddiquee M M R, Tajbakhsh N, et al. UNet++: a nested U-net architecture for medical image segmentation[M]. ∥Stoyanov D, Taylor Z, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science. Cham: Springer, 11045, 3-11(2018).

[12] Jha D, Smedsrud P H, Riegler M A et al. ResUNet++: an advanced architecture for medical image segmentation[C]∥2019 IEEE International Symposium on Multimedia (ISM), December 9-11, 2019, San Diego, CA, USA., 225-2255(2019).

[13] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

[14] Chen L C, Papandreou G, Schroff F et al. -12-05)[2021-02-25]. http:∥arxiv., org/abs/1706, 05587(2017).

[15] Jha D, Riegler M A, Johansen D et al. DoubleU-, 558-564(2020).

[16] Simonyan K. -09-04)[2021-02-25]. https:∥arxiv., org/abs/1409, 1556(2014).

[17] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 3-19(2018).

[18] Li X, Wang W H, Hu X L et al. Selective kernel networks[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 510-519(2019).

[19] [19] DengJ, DongW, SocherR, et al.ImageNet: a large-scale hierarchical image database[C]∥2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE Press, 2009: 248- 255.

[20] 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).

[21] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 3431-3440(2015).

[22] [22] ChaurasiaA, CulurcielloE. LinkNet: exploiting encoder representations for efficient semantic segmentation[C]∥2017 IEEE Visual Communications and Image Processing (VCIP), December 10-13, 2017, St. Petersburg, FL, USA. New York: IEEE Press, 2017: 1- 4.