[1] 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).

[2] [2] 郑荣寿， 孙可欣， 张思维， 等. 2015年中国恶性肿瘤流行情况分析［J］. 中华肿瘤杂志， 2019， 41（1）： 19-28. doi: 10.3760/cma.j.issn.0253-3766.2019.01.005ZHENGR S， SUNK X， ZHANGS W， et al. Report of cancer epidemiology in China， 2015［J］. Chinese Journal of Oncology， 2019， 41（1）： 19-28.（in Chinese）. doi: 10.3760/cma.j.issn.0253-3766.2019.01.005

[3] ARAN V, VICTORINO AP, THULER LC et al. Colorectal cancer： epidemiology， disease mechanisms and interventions to reduce onset and mortality[J]. Clinical Colorectal Cancer, 15, 195-203(2016).

[4] [4] 江贵平， 秦文健， 周寿军， 等. 医学图像分割及其发展现状［J］. 计算机学报， 2015， 38（6）1222-1242. doi: 10.11897/SP.J.1016.2015.01222JIANGG P， QINW J， ZHOUS J， et al. State-of-the-art in medical image segmentation［J］. Chinese Journal of Computers， 2015， 38（6）1222-1242（in Chinese）. doi: 10.11897/SP.J.1016.2015.01222

[5] [5] 田娟秀， 刘国才， 谷珊珊， 等. 医学图像分析深度学习方法研究与挑战［J］. 自动化学报， 2018， 44（3）401-424. doi: 10.16383/j.aas.2018.c170153TIANJ X， LIUG C， GUS S， et al. Deep learning in medical image analysis and its challenges［J］. Acta Automatica Sinica， 2018， 44（3）401-424（in Chinese）. doi: 10.16383/j.aas.2018.c170153

[6] [6] 李少东，洪森，李世权等.人工智能利用图像深度学习诊疗消化道肿瘤的现状及展望［J］.中国普外基础与临床杂志，2021，28（11）：1524-1529. doi: 10.7507/1007-9424.202012127LISH D， HONGS， LISH Q， et al. Current situation and prospect of artificial intelligence in the diagnosis and treatment of. doi: 10.7507/1007-9424.202012127gastrointestinal tumors using image deep learning［J］. Chinese Journal of Bases and Clinics in General Surgery， 2021，28（11）：1524-1529. doi: 10.7507/1007-9424.202012127

[7] [7] 吴锦珍， 谢玥， 王新颖. 人工智能在结直肠息肉性质鉴别中的应用进展［J］. 中华消化内镜杂志， 2020， 37（11）：849-852. doi: 10.3760/cma.j.cn321463-20200610-00515WUJ Z， XIEY， WANGX Y. Application progress of artificial intelligence in identification of colorectal polyps［J］. Chinese Journal of Digestive Endoscopy， 2020， 37（11）： 849-852. （in Chinese）. doi: 10.3760/cma.j.cn321463-20200610-00515

[8] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[9] RONNEBERGER O, FISCHER P, BROX T. U-Net： Convolutional Networks for Biomedical Image Segmentation[M]. Lecture Notes in Computer Science, 234-241(2015).

[10] ZHOU Z, SIDDIQUEE M M R, TAJBAKHSH N et al. UNet++： a Nested U-net Architecture for Medical Image Segmentation[webpage]. arXiv, 1807-10165(2018). https：//arxiv.org/abs/1807.10165

[11] SMEDSRUD P H, RIEGLER M A et al. ResUNet： an advanced architecture for medical image segmentation[C], 225-2255(9).

[12] 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, 263-273(2020).

[13] RIEGLER M A, JOHANSEN D et al. DoubleU-net： a deep convolutional neural network for medical image segmentation[C], 558-564(28).

[14] FANG Y Q, ZHU D L, YAO J H et al. ABC-net： area-boundary constraint network with dynamical feature selection for colorectal polyp segmentation[J]. IEEE Sensors Journal, 21, 11799-11809(2021).

[15] ZHANG R F, LI G B, LI Z et al. Adaptive Context Selection for Polyp Segmentation[M]. Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 253-262(2020).

[16] [16] 刘佳伟， 刘巧红， 李晓欧， 等. 一种改进的双U型网络的结肠息肉分割方法［J］. 光学学报， 2021， 41（18）： 1810001. doi: 10.3788/aos202141.1810001LIUJ W， LIUQ H， LIX O， et al. Improved colonic polyp segmentation method based on double U-shaped network［J］. Acta Optica Sinica， 2021， 41（18）： 1810001.（in Chinese）. doi: 10.3788/aos202141.1810001

[17] [17] 撖子奇， 刘巧红， 凌晨， 等. 结合HarDNet和反向注意力的息肉分割方法［J］. 激光与光电子学进展， 2023， 60（2）： 3788/LOP212665. doi: 10.3788/LOP212665HANZ Q， LIUQ H， LINGC， et al. Polyp segmentation method combining HarDNet and reverse attention［J］. Laser & Optoelectronics Progress， 2023， 60（2）： 3788/LOP212665.（in Chinese）. doi: 10.3788/LOP212665

[18] ZHANG H, WU C R, ZHANG Z Y et al. ResNeSt： split-attention networks[C], 2735-2745(19).

[19] LIU S T, HUANG D, WANG Y H. Receptive Field Block Net for Accurate and Fast Object Detection[M]. Computer Vision-ECCV 2018, 404-419(2018).

[20] ZHU Z, BIAN Z, HOU J et al. When Residual Learning Meets Dense Aggregation： Rethinking the Aggregation of Deep Neural Networks[webpage]. arXiv, 2004-08796(2020). https：//arxiv.org/abs/2004.08796

[21] YAMADA M, SAITO Y, IMAOKA H et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy[J]. Scientific Reports, 9, 14465(2019).

[22] WAINWRIGHT M J, JAAKKOLA T S, WILLSKY A S. MAP estimation via agreement on trees： message-passing and linear programming[J]. IEEE Transactions on Information Theory, 51, 3697-3717(2005).

[23] SZELISKI R, ZABIH R, SCHARSTEIN D et al. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1068-1080(2008).

[24] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(27).

[25] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(18).

[26] LI X, WANG W H, HU X L et al. Selective kernel networks[C], 510-519(15).

[27] XIE S N, GIRSHICK R, DOLLÁR P et al. Aggregated residual transformations for deep neural networks[C], 5987-5995(21).

[28] ZHANG H, ZU K K, LU J et al. EPSANet： an Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network[M]. Computer Vision-ACCV 2022, 541-557(2023).

[29] WU Z, SU L, HUANG Q M. Cascaded partial decoder for fast and accurate salient object detection[C], 3902-3911(15).

[30] KOLMOGOROV V. Convergent tree-reweighted message passing for energy minimization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1568-1583(2006).

[31] YANG X, GAO X B, TAO D C et al. An efficient MRF embedded level set method for image segmentation[J]. IEEE Transactions on Image Processing, 24, 9-21(2015).

[32] [32] 夏平， 施宇， 雷帮军， 等. 复小波域混合概率图模型的超声医学图像分割［J］. 自动化学报， 2021， 47（1）：185-196. doi: 10.16383/j.aas.c180132XIAP， SHIY， LEIB J， et al. Ultrasound medical image segmentation based on hybrid probabilistic graphical model in complex-wavelet domain［J］. Acta Automatica Sinica， 2021， 47（1）：185-196.（in Chinese）. doi: 10.16383/j.aas.c180132

[33] BERNAL J, SÁNCHEZ FJ, 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).

[34] SMEDSRUD P H, RIEGLER M A et al. Kvasir-SEG： a Segmented Polyp Dataset[M]. MultiMedia Modeling, 451-462(2019).

[35] 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).

[36] VÁZQUEZ D, BERNAL J et al. A benchmark for endoluminal scene segmentation of colonoscopy images[J]. Journal of Healthcare Engineering, 2017, 4037190(2017).

[37] LIU L, JIANG H, HE P et al. On the variance of the adaptive learning rate and beyond[J]. arXiv e-prints(2019).

[38] FANG Y Q, CHEN C, YUAN Y X et al. Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation[M]. Lecture Notes in Computer Science, 302-310(2019).

[39] QIN X B, ZHANG Z C, HUANG C Y et al. BASNet： boundary-aware salient object detection[C], 7471-7481(15).

[40] WEI J, WANG S H, HUANG Q M. F³Net： fusion， feedback and focus for salient object detection[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12321-12328(2020).

[41] FAN D P, CHENG M M, LIU Y et al. Structure-measure： a new way to evaluate foreground maps[C], 4558-4567(22).