[1] SIEGEL R L, MILLER K D, WAGLE N S et al. Cancer statistics， 2023[J]. CA： A Cancer Journal for Clinicians, 73, 17-48(2023).

[2] CAI C F, XU J, LIANG L et al. A deep convolutional networks for identifying multiple tissues from colorectal histologic image of whole slide[J]. Chinese Journal of Biomedical Engineering, 36, 632-636(2017).

     蔡程飞, 徐军, 梁莉. 基于深度卷积网络的结直肠全扫描病理图像的多种组织分割[J]. 中国生物医学工程学报, 36, 632-636(2017).

[3] 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(2020).

[4] NASSAR D, BLANPAIN C. Cancer stem cells： basic concepts and therapeutic implications[J]. Annual Review of Pathology, 11, 47-76(2016).

[5] HAMIDA ABEN, DEVANNE M, WEBER J et al. Deep learning for colon cancer histopathological images analysis[J]. Computers in Biology and Medicine, 136, 104730(2021).

[6] SIEGEL R L, MILLER K D, FUCHS H E et al. Cancer statistics， 2021[J]. CA： A Cancer Journal for Clinicians, 71, 7-33(2021).

[7] LEE G, SPARKS R et al. Co-occurring gland angularity in localized subgraphs： predicting biochemical recurrence in intermediate-risk prostate cancer patients[J]. PLoS One, 9(2014).

[8] VERMA R, KUMAR N, SETHI A et al. Detecting multiple sub-types of breast cancer in a single patient[C], 2648-2652(2016).

[9] HANAHAN D, WEINBERG R A. Hallmarks of cancer： the next generation[J]. Cell, 144, 646-674(2011).

[10] SKREDE O J, DE RAEDT S, KLEPPE A et al. Deep learning for prediction of colorectal cancer outcome： a discovery and validation study[J]. The Lancet, 395, 350-360(2020).

[11] ABDULJABBAR K, AHMED RAZA S E, ROSENTHAL R et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma[J]. Nature Medicine, 26, 1054-1062(2020).

[12] QIN C B, SONG Z Y, ZENG J Y et al. Deeply supervised breast cancer segmentation combined with multi-scale and attention-residuals[J]. Opt. Precision Eng., 29, 877(2021).

     秦传波, 宋子玉, 曾军英. 联合多尺度和注意力-残差的深度监督乳腺癌分割[J]. 光学 精密工程, 29, 877(2021).

[13] 徐昌佳, 易见兵, 曹锋. 采用DoubleUNet网络的结直肠息肉分割算法[J]. 光学 精密工程, 30, 970-983(2022).

     XU C J, YI J B, CAO F et al. Colorectal polyp segmentation algorithm using DoubleUNet network[J]. Opt. Precision Eng., 30, 970-983(2022).

[14] LIANG L M, HE A J, LI R J et al. Cross-scale and cross-dimensional adaptive transformer network for colorectal polyp segmentation[J]. Opt. Precision Eng., 31, 2700-2712(2023).

     梁礼明, 何安军, 李仁杰. 跨尺度跨维度的自适应Transformer网络应用于结直肠息肉分割[J]. 光学 精密工程, 31, 2700-2712(2023).

[15] RONNEBERGER O, FISCHER P, BROX T[M]. U-Net： Convolutional Networks for Biomedical Image Segmentation, 234-241(2015).

[16] GUO Y, MATUSZEWSKI B. GIANA Polyp Segmentation with Fully Convolutional Dilation Neural Networks[C], 25, 632-641(2019).

[17] SUN X Z, ZHANG P F, WANG D C et al. Colorectal polyp segmentation by U-Net with dilation convolution[C], 16, 851-858(2019).

[18] ZHOU X, BAI Z Y, LU Q J et al. Colorectal polyp segmentation based on group convolution and transformer[C], 22, 6387-6392(2021).

[19] LIN A L, CHEN B Z, XU J Y et al. DS-TransUNet： dual swin transformer U-Net for medical image segmentation[J]. IEEE Transactions on Instrumentation and Measurement, 71, 4005615(2022).

[20] OUALI Y, HUDELOT C, TAMI M. Semi-supervised semantic segmentation with cross-consistency training[C], 13, 12674-12684(2020).

[21] ZHANG Y, ZHOU B, CHEN L et al. Multi-transformation consistency regularization for semi-supervised medical image segmentation[C], 28, 485-489(2021).

[22] SHEN Z Q, CAO P, YANG H et al. Co-training with High-Confidence Pseudo Labels for Semi-Supervised Medical Image Segmentation[webpage]. arXiv, 2301-04465(2023). https：//arxiv.org/abs/2301.04465v3

[23] TARVAINEN A, VALPOLA H. Mean teachers are better role models： weight-averaged consistency targets improve semi-supervised deep learning results[C](2017).

[24] CHEN X K, YUAN Y H, ZENG G et al. Semi-supervised semantic segmentation with cross pseudo supervision[C], 20, 2613-2622(2021).

[25] YANG L H, ZHAO Z, QI L et al. Shrinking class space for enhanced certainty in semi-supervised learning[C], 16141-16150(2023).

[26] WANG Y C, WANG H C, SHEN Y J et al. Semi-supervised semantic segmentation using unreliable pseudo-labels[C], 4238-4247(2022).

[27] YANG L H, ZHUO W, QI L et al. ST： Make Self-Trainingwork better for semi-supervised semantic segmentation[C], 4258-4267(2022).

[28] DEVRIES T, TAYLOR G W, ASSIRI Y. Improved Regularization of Convolutional Neural Networks with Cutout[webpage]. arXiv, 1708-04552(2017). https：//arxiv.org/abs/1708.04552v2

[29] YUN S, HAN D, CHUN S et al. CutMix： regularization strategy to train strong classifiers with localizable features[C], 6022-6031(2019).

[30] ZHAO Z, YANG L H, LONG S F et al. Augmentation matters： a simple-yet-effective approach to semi-supervised semantic segmentation[C], 11350-11359(2023).

[31] HAN C, LIN J T, MAI J H et al. Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels[J]. Medical Image Analysis, 80, 102487(2022).

[32] KWON D, KWAK S. Semi-supervised semantic segmentation with error localization network[C], 9947-9957(2022).

[33] YANG L H, QI L, FENG L T et al. Revisiting Weak-To-Strong consistency in semi-supervised semantic segmentation[C], 7236-7246(2023).