[1] HEALTH ORGANIZATION WORLD. International agen-cy for research on cancer(2023).
[2] 中华医学会肿瘤学分会早诊早治学组. 中国结直肠癌早诊早治专家共识（2023版）［J］. 中华医学杂志， 2023， 103（48）： 3896-3908.Expert consensus on the early diagnosis and treatment of colorectal cancer in China （2023 edition）［J］. National Medical Journal of China， 2023， 103（48）： 3896-3908.（in Chinese）
[3] R L SIEGEL, N S WAGLE, A CERCEK et al. Colorectal cancer statistics， 2023. CA： A Cancer Journal for Clinicians, 73, 233-254(2023).
[4] 孙福艳， 王琼， 吕宗旺， 等. 深度学习在结肠息肉分割中的应用综述［J］. 计算机工程与应用， 2023， 59（23）： 15-27.SUNF Y， WANGQ， （LÜ/LV/LU/LYU）Z W， et al. Review of application of deep learning in colon polyp segmentation［J］. Computer Engineering and Applications， 2023， 59（23）： 15-27.（in Chinese）
[5] D P FAN, G P JI, T ZHOU et al. Pranet： Parallel Reverse Attention Network for Polyp Segmentation, 263-273(2020).
[6] N K TOMAR, U BAGCI et al. Tganet： Text-Guided Attention for Improved Polyp Segmentation, 151-160(2022).
[7] G H YUE, W W HAN, B JIANG et al. Boundary constraint network with cross layer feature integration for polyp segmentation. IEEE Journal of Biomedical and Health Informatics, 26, 4090-4099(2022).
[8] Z J YIN, K M LIANG, Z Y MA et al. Duplex Contextual Relation Network for Polyp Segmentation, 1-5(2022).
[9] T ZHOU, Y ZHOU, K L HE et al. Cross-level feature aggregation network for polyp segmentation. Pattern Recognition, 140, 109555(2023).
[10] J F WANG, Q M HUANG, F L TANG et al. Stepwise Feature Fusion： Local Guides Global, 110-120(2022).
[11] 周雪，柏正尧，陆倩杰，等. 融合Transformer和轴向注意的结直肠息肉分割［J］. 计算机工程与应用， 2023， 59（11）： 222-230.ZHOUX， BAIZ Y， LUQ J， et al. Colorectal polyp segm-entation combining pyramid vision transformer and axial attention［J］. Computer Engineering and Applications， 2023， 59（11）： 222-230. （in Chinese）
[12] 梁礼明， 何安军， 董信， 等. 融合PVTv2和多尺度边界聚合的结直肠息肉分割算法［J］. 计算机应用研究， 2023， 40（5）： 1553-1558.LIANGL M， HEA J， DONGX， et al. Colorectal polyp segmentation algorithm fusion PVTv2 and multiscale boundary aggregation［J］. Application Research of Computers， 2023， 40（5）： 1553-1558.（in Chinese）
[13] Y D ZHANG, H Y LIU, Q HU. Transfuse： fusing transformers and cnns for medical image segmentation, 14-24(2021).
[14] E SANDERSON, B J MATUSZEWSKI. FCN-Transformer feature fusion for polyp segmentation, 892-907(2022).
[15] T KIM, H LEE, D KIM. UACANet： Uncertainty augmented context attention for polyp segmentation, 2167-2175(2021).
[16] H LI, L YANG, J MIAO et al. MCE-Net： polyp segmentation with multiple branch series-parallel attention and channel interaction via edge distribution guidance. Physics in Medicine &, 68, 135003(2023).
[17] E XIE, W WANG, Z YU et al. SegFormer： Simple and efficient design for semantic segmentation with transfor-mers. Advances in neural information processing systems, 34, 12077-12090(2021).
[18] M TAN, Q LE. Efficientnet： rethinking model scaling for convolutional neural networks, 6105-6114(2019).
[19] C Y LEE, S XIE, P GALLAGHER et al. Deeply-supervised nets, 562-570(2015).
[20] Y J PENG, T ZHANG, Y F GUO. Cov-TransNet： dual branch fusion network with transformer for COVID-19 infection segmentation. Biomedical Signal Processing and Control, 80, 104366(2023).
[21] Q H LIU, Y J LIN, X X HAN et al. Dual-branch feature extraction network combined with Transformer and CNN for polyp segmentation. International Journal of Imaging Systems and Technology, 34, e22987(2024).
[22] J FU, J LIU, H J TIAN et al. Dual Attention Network For Scene Segmentation, 3141-3149(2019).
[23] Y XIA, H J YUN, Y J LIU et al. MGCBFormer： the multiscale grid-prior and class-inter boundary-aware transformer for polyp segmentation. Computers in Biology and Medicine, 167, 107600(2023).
[24] A SRIVASTAVA, S CHANDA et al. GMSRF-Net： an improved generalizability with global multi-scale residual fusion network for polyp segmentation, 4321-4327(2022).
[25] Q CHANG, D AHMAD, J TOTH et al. ESFPNet： efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video, 19, 2023(2023).
[26] N CARION, F MASSA, G SYNNAEVE et al. End-To-End object detection with transformers, 213-229(2020).
[27] J BERNAL, F J SÁNCHEZ, G FERNÁNDEZ-ESPARRACH et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy： validation
[28] P H SMEDSRUD, M A RIEGLER et al. Kvasir-seg： a segmented polyp dataset. Springer International Publishing, 451-462(2020).
[29] D VÁZQUEZ, J BERNAL, F J SÁNCHEZ et al. A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of Healthcare Engineering, 2017, 4037190(2017).
[30] N TAJBAKHSH, S R GURUDU, J M LIANG. Automated polyp detection in colonoscopy videos using shape and context information. IEEE Transactions on Medical Imaging, 35, 630-644(2016).
[31] J WEI, S H WANG, Q M HUANG. F³Net： fusion， feedback and focus for salient object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12321-12328(2020).
[32] O RONNEBERGER, P FISCHER, T BROX. U-net： convolutional networks for biomedical image segmentation, 234-241(2015).
[33] Z W ZHOU, M M R SIDDIQUEE, N TAJBAKHSH et al. UNet++： redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39, 1856-1867(2020).
[34] Z W GU, J CHENG, H Z FU et al. CE-net： context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging, 38, 2281-2292(2019).
[35] H S WU, Z B ZHAO, Z Z WANG. META-unet： multi-scale efficient transformer attention unet for fast and high-accuracy polyp segmentation. IEEE Transactions on Automation Science and Engineering, 21, 4117-4128(2024).
[36] J W ZHENG, Y D YAN, L ZHAO et al. CGMA-net： cross-level guidance and multi-scale aggregation network for polyp segmentation. IEEE Journal of Biomedical and Health Informatics, 28, 1424-1435(2024).
[37] N T DUC, N T OANH, N T THUY et al. ColonFormer： an efficient transformer based method for colon polyp segmentation. IEEE Access, 10, 80575-80586(2022).
[38] X B JIANG, Y ZHU, Y T LIU et al. MC-DC： an MLP-CNN based dual-path complementary network for medical image segmentation. Computer Methods and Programs in Biomedicine, 242, 107846(2023).
[39] P F SONG, J J LI, H FAN et al. TGDAUNet： transformer and GCNN based dual-branch attention UNet for medical image segmentation. Computers in Biology and Medicine, 167, 107583(2023).
