Review of Application of Deep Learning in Cervical Cell Segmentation

doi:10.3778/j.issn.1673-9418.2407079

Abstract

Abstract: Cervical cancer is a common malignant tumor that threatens women’s life and health. Its early diagnosis and treatment are very important for patients’ life safety. However, due to the shortcomings of traditional manual examination in efficiency and consistency of results, it is urgent to use computer-aided technology to improve the accuracy and efficiency of diagnosis. In recent years, the rapid development of deep learning technology has been applied to the field of cervical cell segmentation, which has greatly improved the accuracy and speed of segmentation, and thus significantly improved the accuracy and efficiency of cervical cytology examination, providing strong technical support for the early diagnosis of cervical cancer. In order to better understand the research status and progress of deep learning technology in the field of cervical cell segmentation, firstly, the widely used public cervical cell segmentation datasets are summarized. At the same time, the commonly used evaluation indicators are systematically summarized to better understand the performance of different models. Then, the specific application of deep learning technology in the field of cervical cell segmentation is discussed, and the main improvement strategies, actual effects and limitations of different algorithms are compared in detail. Finally, the current challenges and problems in this field are analyzed, and the future research direction is proposed.

Key words: cervical cancer, computer-aided technology, deep learning, cervical cell segmentation

摘要： 宫颈癌作为一种常见的严重威胁女性生命健康的恶性肿瘤，其早期诊断和治疗对患者的生命安全至关重要。然而，由于传统的人工检查在效率和结果一致性上存在不足，迫切需要利用计算机辅助技术来提升诊断的准确性和效率。近年来，深度学习技术飞速发展，将其应用于宫颈细胞分割领域，极大提高了分割的精确度和速度，进而显著提升了宫颈细胞学检查的准确性与效率，为宫颈癌的早期诊断提供了强有力的技术支持。为了更好地了解深度学习技术在宫颈细胞分割领域的研究现状和进展，对当前广泛使用的公开的宫颈细胞分割数据集进行了总结，对常用的评价指标进行了系统归纳，以便更好地理解不同模型的性能表现。深入探讨了深度学习技术在宫颈细胞分割领域的具体应用，并对不同算法的主要改进策略、实际效果及其局限性进行了详尽的比较分析。对该领域当前所面临的挑战和问题进行了剖析，并对未来的研究方向提出了展望。

关键词: 宫颈癌, 计算机辅助技术, 深度学习, 宫颈细胞分割

ZHU Jiayin, LI Yang, LI Ming, MA Jingang. Review of Application of Deep Learning in Cervical Cell Segmentation[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(6): 1476-1493.

朱佳音, 李杨, 李明, 马金刚. 深度学习在宫颈细胞分割中的应用综述[J]. 计算机科学与探索, 2025, 19(6): 1476-1493.

References

[1] SINGH D, VIGNAT J, LORENZONI V, et al. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative[J]. The Lancet Global Health, 2023, 11(2): 197-206.
[2] 王苏蒙, 闫慧姣, 任文辉, 等. 中国宫颈癌防控进展[J]. 中国预防医学杂志, 2023, 24(12): 1366-1370.
WANG S M, YAN H J, REN W H, et al. Progress in cervical cancer prevention and control in China[J]. Chinese Preventive Medicine, 2023, 24(12): 1366-1370.
[3] WENTZENSEN N, CLARKE M A. Cervical cancer screening-past, present, and future[J]. Cancer Epidemiology, Biomarkers & Prevention, 2021, 30(3): 432-434.
[4] 李艳, 王楠, 张青, 等. 宫颈病变患者高危HPV感染筛查方法及应用[J]. 中华医院感染学杂志, 2023, 33(22): 3461-3465.
LI Y, WANG N, ZHANG Q, et al. Screening methods of high-risk HPV infection among cervical lesion patients and their application[J]. Chinese Journal of Nosocomiology, 2023, 33(22): 3461-3465.
[5] MREMI A, MCHOME B, MLAY J, et al. Performance of HPV testing, Pap smear and VIA in women attending cervical cancer screening in Kilimanjaro region, Northern Tanzania: a cross-sectional study nested in a cohort[J]. BMJ Open, 2022, 12(10): e064321.
[6] 吴志丽, 徐岚. 宫颈癌早期筛查与预防的研究进展[J]. 癌变·畸变·突变, 2023, 35(4): 310-315.
WU Z L, XU L. Research progress of early screening and prevention of cervical cancer[J]. Carcinogenesis, Teratogenesis & Mutagenesis, 2023, 35(4): 310-315.
[7] 吴嘉敏, 郑睿敏, 马兰, 等. 我国部分地区妇女“两癌”筛查现状分析[J]. 中国健康教育, 2022, 38(4): 334-338.
WU J M, ZHENG R M, MA L, et al. Analysis on the status of the screening for cervical cancer and breast cancer of women in some region of China[J]. Chinese Journal of Health Education, 2022, 38(4): 334-338.
[8] 林霞. 两癌筛查对女性健康的作用[J]. 中国医药指南, 2023, 21(30): 67-69.
LIN X. The effect of two cancer screening on women’s health[J]. Guide of China Medicine, 2023, 21(30): 67-69.
[9] CHEN H, LIU J, WEN Q M, et al. CytoBrain: cervical cancer screening system based on deep learning technology[J]. Journal of Computer Science and Technology, 2021, 36(2): 347-360.
[10] 姜良, 张程, 魏德健, 等. 深度学习在骨质疏松辅助诊断中的应用[J]. 计算机工程与应用, 2024, 60(7): 26-40.
JIANG L, ZHANG C, WEI D J, et al. Deep learning in aided diagnosis of osteoporosis[J]. Computer Engineering and Applications, 2024, 60(7): 26-40.
[11] RAHAMAN M M, LI C, WU X C, et al. A survey for cervical cytopathology image analysis using deep learning[J]. IEEE Access, 2020, 8: 61687-61710.
[12] LI C, CHEN H, LI X Y, et al. A review for cervical histopathology image analysis using machine vision approaches[J]. Artificial Intelligence Review, 2020, 53(7): 4821-4862.
[13] JIANG P, LI X K, SHEN H, et al. A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis[J]. Artificial Intelligence Review, 2023, 56(2): 2687-2758.
[14] JANTZEN J, NORUP J, DOUNIAS G, et al. Papsmear benchmark data for pattern classification[C]//Proceedings of the Nature Inspired Smart Information Systems, 2005: 1-9.
[15] LU Z, CARNEIRO G, BRADLEY A P, et al. Evaluation of three algorithms for the segmentation of overlapping cervical cells[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 21(2): 441-450.
[16] CHEN J, ZHANG B. Segmentation of overlapping cervical cells with mask region convolutional neural network[J]. Computational and Mathematical Methods in Medicine, 2021(1): 3890988.
[17] PLISSITI M E, DIMITRAKOPOULOS P, SFIKAS G, et al. Sipakmed: a new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images[C]//Proceedings of the 2018 25th IEEE International Conference on Image Processing. Piscataway: IEEE, 2018: 3144-3148.
[18] LIU G Q, DING Q H, LUO H B, et al. Cx22: a new publicly available dataset for deep learning-based segmentation of cervical cytology images[J]. Computers in Biology and Medicine, 2022, 150: 106194.
[19] ZHAO J, HE Y J, ZHOU S H, et al. CNSeg: a dataset for cervical nuclear segmentation[J]. Computer Methods and Programs in Biomedicine, 2023, 241: 107732.
[20] WANG Z B, WANG E, ZHU Y. Image segmentation evaluation: a survey of methods[J]. Artificial Intelligence Review, 2020, 53(8): 5637-5674.
[21] KHUDOV H, MAKOVEICHUK O, KHIZHNYAK I, et al. The choice of quality indicator for the image segmentation evaluation[J]. International Journal of Emerging Technology and Advanced Engineering, 2022, 12(10): 95-103.
[22] HENDERSON P, FERRARI V. End-to-end training of object class detectors for mean average precision[C]//Proceedings of the 13th Asian Conference on Computer Vision. Cham: Springer, 2017: 198-213.
[23] ZIJDENBOS A P, DAWANT B M, MARGOLIN R A, et al. Morphometric analysis of white matter lesions in MR images: method and validation[J]. IEEE Transactions on Medical Imaging, 1994, 13(4): 716-724.
[24] KUMAR N, VERMA R, SHARMA S, et al. A dataset and a technique for generalized nuclear segmentation for computational pathology[J]. IEEE Transactions on Medical Imaging, 2017, 36(7): 1550-1560.
[25] KIRILLOV A, HE K M, GIRSHICK R, et al. Panoptic segmentation[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 9396-9405.
[26] MINAEE S, BOYKOV Y, PORIKLI F, et al. Image segmentation using deep learning: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(7): 3523-3542.
[27] WANG X Z, ZHAO Y X, POURPANAH F. Recent advances in deep learning[J]. International Journal of Machine Learning and Cybernetics, 2020, 11(4): 747-750.
[28] LI Z W, LIU F, YANG W J, et al. A survey of convolutional neural networks: analysis, applications, and prospects[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(12): 6999-7019.
[29] SULTANA F, SUFIAN A, DUTTA P. Evolution of image segmentation using deep convolutional neural network: a survey[J]. Knowledge-Based Systems, 2020, 201: 106062.
[30] 郭佳霖, 智敏, 殷雁君, 等. 图像处理中CNN与视觉Transformer混合模型研究综述[J]. 计算机科学与探索, 2025, 19(1): 30-44.
GUO J L, ZHI M, YIN Y J, et al. Review of research on CNN and visual Transformer hybrid models in image processing[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(1): 30-44.
[31] ZHAO Y L, FU C, ZHANG W C, et al. Automatic segmentation of cervical cells based on star-convex polygons in pap smear images[J]. Bioengineering, 2023, 10(1): 47.
[32] HAO X L, PEI L L, LI W, et al. An improved cervical cell segmentation method based on deep convolutional network[J]. Mathematical Problems in Engineering, 2022(1): 7383573.
[33] WAN T, XU S S, SANG C, et al. Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks[J]. Neurocomputing, 2019, 365: 157-170.
[34] HU H X, ZHANG J Y, YANG T J, et al. CNAC-Seg: effective segmentation for cervical nuclei in adherent cells and clusters via exploring gaps of receptive fields[J]. Biomedical Signal Processing and Control, 2024, 90: 105833.
[35] LUO D, KANG H T, LONG J N, et al. Dual supervised sampling networks for real-time segmentation of cervical cell nucleus[J]. Computational and Structural Biotechnology Journal, 2022, 20: 4360-4368.
[36] XU S S, SANG C, JIN Y L, et al. Robust segmentation of overlapping cells in cervical cytology using light convolution neural network[C]//Proceedings of the 25th International Conference on Neural Information Processing. Cham: Springer, 2018: 387-397.
[37] YIN Y Y, LUO S L, ZHOU J, et al. LDCNet: lightweight dynamic convolution network for laparoscopic procedures image segmentation[J]. Neural Networks, 2024, 170: 441-452.
[38] GUAN R C, WANG M M, BRUZZONE L, et al. Lightweight attention network for very high-resolution image semantic segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4403514.
[39] ZHANG B Z, WANG X Y, LIU L G, et al. CeLNet: a correlation-enhanced lightweight network for medical image segmentation[J]. Physics in Medicine & Biology, 2023, 68(11): 115012.
[40] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
[41] 崔珂, 田启川, 廉露. 基于U-Net变体的医学图像分割算法综述[J]. 计算机工程与应用, 2024, 60(11): 32-49.
CUI K, TIAN Q C, LIAN L. Review of medical image segmentation algorithms based on U-Net variants[J]. Computer Engineering and Applications, 2024, 60(11): 32-49.
[42] GUO M H, XU T X, LIU J J, et al. Attention mechanisms in computer vision: a survey[J]. Computational Visual Media, 2022, 8(3): 331-368.
[43] 祁宣豪, 智敏. 图像处理中注意力机制综述[J]. 计算机科学与探索, 2024, 18(2): 345-362.
QI X H, ZHI M. Review of attention mechanisms in image processing[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(2): 345-362.
[44] CHOWDARY G J, SUGANYA G, PREMALATHA M, et al. Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images[J]. Technology in Cancer Research & Treatment, 2023, 22.
[45] 张玉琦, 李捷, 王巍, 等. 基于注意力机制的多尺度特征融合网络用于宫颈细胞核分割[J]. 计算机应用, 2022, 42(S2): 259-266.
ZHANG Y Q, LI J, WANG W, et al. Multi-scale feature fusion network for cervical nucleus segmentation based on attention mechanism[J]. Journal of Computer Applications, 2022, 42(S2): 259-266.
[46] ZHAO J, HE Y J, ZHAO S Q, et al. AL-net: attention learning network based on multi-task learning for cervical nucleus segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(6): 2693-2702.
[47] 令狐鑫瑶, 陈燕, 张鹏程, 等. 基于多尺度引导滤波的宫颈细胞核图像分割[J]. 计算机应用, 2025, 45(4): 1333-1339.
LINGHU X Y, CHEN Y, ZHANG P C, et al. Cervical cell nucleus image segmentation based on multi-scale guided filtering[J]. Journal of Computer Applications, 2025, 45(4): 1333-1339.
[48] LI G, SUN C J, XU C Y, et al. Cervical cell segmentation method based on global dependency and local attention[J]. Applied Sciences, 2022, 12(15): 7742.
[49] XIE L P, QI J, PAN L L, et al. Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images[J]. Neurocomputing, 2020, 376: 166-179.
[50] STRINGER C, WANG T, MICHAELOS M, et al. Cellpose: a generalist algorithm for cellular segmentation[J]. Nature Methods, 2020, 18(1): 100-106.
[51] YANG G H, HUANG J J, HE Y J, et al. GCP-net: a gating context-aware pooling network for cervical cell nuclei segmentation[J]. Mobile Information Systems, 2022(1): 7511905.
[52] RASHEED A, SHIRAZI S H, UMAR A I, et al. Cervical cell’s nucleus segmentation through an improved UNet architecture[J]. PLoS One, 2023, 18(10): e0283568.
[53] XIANG Y, SUN W X, PAN C L, et al. A novel automation-assisted cervical cancer reading method based on convolutional neural network[J]. Biocybernetics and Biomedical Engineering, 2020, 40(2): 611-623.
[54] ZHANG E G, XIE R X, BIAN Y X, et al. Cervical cell nuclei segmentation based on GC-UNet[J]. Heliyon, 2023, 9(7): e17647.
[55] HUSSAIN E, MAHANTA L B, DAS C R, et al. A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images[J]. Artificial Intelligence in Medicine, 2020, 107: 101897.
[56] GROSSER S, LIPPOLDT J, OSWALD L, et al. Cell and nucleus shape as an indicator of tissue fluidity in carcinoma[J]. Physical Review X, 2021, 11(1): 011033.
[57] ZHANG H, ZHU H Q, LING X F. Polar coordinate sampling-based segmentation of overlapping cervical cells using attention U-Net and random walk[J]. Neurocomputing, 2020, 383: 212-223.
[58] GRADY L. Random walks for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(11): 1768-1783.
[59] 朱琳琳, 韩璐, 杜泓, 等. 基于U-Net网络的多主动轮廓细胞分割方法研究[J]. 红外与激光工程, 2020, 49(S1): 20200121.
ZHU L L, HAN L, DU H, et al. Multi-active contour cell segmentation method based on U-Net network[J]. Infrared and Laser Engineering, 2020, 49(S1): 20200121.
[60] O’MAHONY N, CAMPBELL S, CARVALHO A, et al. Deep learning vs. traditional computer vision[C]//Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference. Cham: Springer, 2019: 128-144.
[61] HE K M, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2980-2988.
[62] BHARATI P, PRAMANIK A. Deep learning techniques: R-CNN to Mask R-CNN: a survey[C]//Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2019. Singapore: Springer, 2020: 657-668.
[63] ZHANG Y Q, CHU J, LENG L, et al. Mask-refined R-CNN: a network for refining object details in instance segmentation[J]. Sensors, 2020, 20(4): 1010.
[64] CHEN J J, ZHANG B C. Segmentation of overlapping cervical cells with mask region convolutional neural network[J]. Computational and Mathematical Methods in Medicine, 2021(1): 3890988.
[65] ZHANG B C, WANG W F, ZHAO W, et al. An improved approach for automated cervical cell segmentation with PointRend[J]. Scientific Reports, 2024, 14: 14210.
[66] LIU Y M, ZHANG P C, SONG Q C, et al. Automatic segmentation of cervical nuclei based on deep learning and a conditional random field[J]. IEEE Access, 2018, 6: 53709-53721.
[67] 郑杨, 梁光明, 刘任任. 基于Mask R-CNN的宫颈细胞图像分割[J]. 计算机时代, 2020(10): 68-72.
ZHENG Y, LIANG G M, LIU R R. Cervical cell image segmentation based on Mask R-CNN[J]. Computer Era, 2020(10): 68-72.
[68] 李静, 张悦, 乔亚鑫, 等. 基于改进Mask R-CNN模型的宫颈细胞分割[J]. 长春理工大学学报(自然科学版), 2024, 47(2): 107-113.
LI J, ZHANG Y, QIAO Y X, et al. Cervical cell segmentation based on improved Mask R-CNN model[J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2024, 47(2): 107-113.
[69] KURNIANINGSIH, ALLEHAIBI K H S, NUGROHO L E, et al. Segmentation and classification of cervical cells using deep learning[J]. IEEE Access, 2019, 7: 116925-116941.
[70] DE LIMA C R, KHAN S G, SHAH S H, et al. Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations[J]. Heliyon, 2023, 9(11): e21388.
[71] FUJITA S, HAN X H. Cell detection and segmentation in microscopy images with improved mask R-CNN[C]//Proceedings of the 15th Asian Conference on Computer Vision. Cham: Springer, 2021: 58-70.
[72] LOH D R, YONG W X, YAPETER J, et al. A deep learning approach to the screening of malaria infection: automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN[J]. Computerized Medical Imaging and Graphics, 2021, 88: 101845.
[73] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems 27, 2014: 2672-2680.
[74] AGGARWAL A, MITTAL M, BATTINENI G. Generative adversarial network: an overview of theory and applications[J]. International Journal of Information Management Data Insights, 2021, 1(1): 100004.
[75] GUI J, SUN Z N, WEN Y G, et al. A review on generative adversarial networks: algorithms, theory, and applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(4): 3313-3332.
[76] HE Z H, JIA D Y, ZHANG C W, et al. A two-stage approach solo_GAN for overlapping cervical cell segmentation based on single-cell identification and boundary generation[J]. Applied Intelligence, 2024, 54(6): 4621-4645.
[77] HUANG J J, YANG G H, LI B, et al. Segmentation of cervical cell images based on generative adversarial networks[J]. IEEE Access, 2021, 9: 115415-115428.
[78] BRIA A, MARROCCO C, TORTORELLA F. Addressing class imbalance in deep learning for small lesion detection on medical images[J]. Computers in Biology and Medicine, 2020, 120: 103735.
[79] QASIM A B, EZHOV I, SHIT S, et al. Red-GAN: attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI[C]//Proceedings of the 2020 International Conference on Medical Imaging with Deep Learning, 2020: 655-668.
[80] REZAEI M, YANG H, MEINEL C. Conditional generative refinement adversarial networks for unbalanced medical image semantic segmentation[EB/OL]. [2024-05-26]. https://arXiv.org/abs/1810.03871.
[81] TELLEZ D, LITJENS G, BáNDI P, et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology[J]. Medical Image Analysis, 2019, 58: 101544.
[82] ROY S, KUMAR JAIN A, LAL S, et al. A study about color normalization methods for histopathology images[J]. Micron, 2018, 114: 42-61.
[83] CHEN X H, YU J Y, CHENG S H, et al. An unsupervised style normalization method for cytopathology images[J]. Computational and Structural Biotechnology Journal, 2021, 19: 3852-3863.
[84] KANG H T, LUO D, FENG W H, et al. StainNet: a fast and robust stain normalization network[J]. Frontiers in Medicine, 2021, 8: 746307.
[85] YANG S R, XIAO W K, ZHANG M C, et al. Image data augmentation for deep learning: a survey[EB/OL]. [2024-05-26]. https://arXiv.org/abs/2204.08610.
[86] MAHARANA K, MONDAL S, NEMADE B. A review: data pre-processing and data augmentation techniques[J]. Global Transitions Proceedings, 2022, 3(1): 91-99.
[87] CHEN S Y, GAO D R, WANG L T, et al. Cervical cancer single cell image data augmentation using residual condition generative adversarial networks[C]//Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Big Data. Piscataway: IEEE, 2020: 237-241.
[88] PANDEY S, SINGH P R, TIAN J. An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation[J]. Biomedical Signal Processing and Control, 2020, 57: 101782.
[89] POURSAEED O, JIANG T X, YANG H, et al. Robustness and generalization via generative adversarial training[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 15691-15700.
[90] 汪美琴, 袁伟伟, 张继业. 生成对抗网络GAN的研究综述[J]. 计算机工程与设计, 2021, 42(12): 3389-3395.
WANG M Q, YUAN W W, ZHANG J Y. Overview of research on generative adversarial network GAN[J]. Computer Engineering and Design, 2021, 42(12): 3389-3395.
[91] XIAO H G, LI L, LIU Q Y, et al. Transformers in medical image segmentation: a review[J]. Biomedical Signal Processing and Control, 2023, 84: 104791.
[92] CHENG B W, MISRA I, SCHWING A G, et al. Masked-attention mask transformer for universal image segmentation[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 1280-1289.
[93] 方红, 李德生, 蒋广杰. 高效跨域的Transformer小样本语义分割网络[J]. 计算机工程与应用, 2024, 60(4): 142-152.
FANG H, LI D S, JIANG G J. Efficient cross-domain transformer few-shot semantic segmentation network[J]. Computer Engineering and Applications, 2024, 60(4): 142-152.
[94] HU H X, ZHANG J Y, YANG T J, et al. PATrans: pixel-adaptive Transformer for edge segmentation of cervical nuclei on small-scale datasets[J]. Computers in Biology and Medicine, 2024, 168: 107823.
[95] 张灿, 陈玮, 尹钟. 基于弱监督宫颈细胞图像的语义分割方法[J]. 电子科技, 2021, 34(12): 68-74.
ZHANG C, CHEN W, YIN Z. Semantic segmentation of cervical cell image based on weak supervision[J]. Electronic Science and Technology, 2021, 34(12): 68-74.
[96] HUANG Y M, ZHU H Q. Segmentation of overlapped cervical cells using asymmetric mixture model and shape constraint level set method[J]. Mathematical Problems in Engineering, 2020(1): 3728572.
[97] YANG X N, DING B, QIN J, et al. HVS-Unsup: unsupervised cervical cell instance segmentation method based on human visual simulation[J]. Computers in Biology and Medicine, 2024, 171: 108147.
[98] 马冬梅, 李悦媛, 陈曦. 融合双重极化注意力的轻量化半监督语义分割[J]. 计算机工程与应用, 2024, 60(8): 225-233.
MA D M, LI Y Y, CHEN X. Lightweight semi-supervised semantic segmentation algorithm based on dual-polarization self-attention[J]. Computer Engineering and Applications, 2024, 60(8): 225-233.
[99] PARK W, HUH J K, LEE J H. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset[J]. Scientific Reports, 2023, 13: 4862.
[100] CAI Q, MA M X, WANG C, et al. Image neural style transfer: a review[J]. Computers and Electrical Engineering, 2023, 108: 108723.
[101] 沈秭扬, 倪欢, 管海燕. 遥感图像跨域语义分割的无监督域自适应对齐方法[J]. 测绘学报, 2023, 52(12): 2115-2126.
SHEN Z Y, NI H, GUAN H Y. Unsupervised domain adaptation alignment method for cross-domain semantic segmentation of remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(12): 2115-2126.