Chinese Journal of Lasers, Volume. 51, Issue 3, 0307108(2024)

Automatic Identification of Cervical Abnormal Cells Based on Transformer

Zheng Zhang1, Mingxiao Chen1, Xinyu Li1, Yi Chen1, Shuwei Shen2, and Peng Yao3、aff***
Author Affiliations
  • 1Department of Precision Machinery and Precision Instrumentation, School of Engineering Science, University of Science and Technology of China, Hefei 230027, Anhui , China
  • 2Suzhou Advanced Research Institute, University of Science and Technology of China, Suzhou 215123, Jiangsu ,China
  • 3School of Microelectronics, University of Science and Technology of China, Hefei 230027, Anhui , China
  • show less
    References(35)

    [1] Sung H, Ferlay J, Siegel R L et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 71, 209-249(2021).

    [2] de Bekker-Grob E W, de Kok I M C M, Bulten J et al. Liquid-based cervical cytology using ThinPrep technology: weighing the pros and cons in a cost-effectiveness analysis[J]. Cancer Causes & Control, 23, 1323-1331(2012).

    [3] Elsheikh T M, Austin R M, Chhieng D F et al. American Society of Cytopathology workload recommendations for automated Pap test screening: developed by the productivity and quality assurance in the era of automated screening task force[J]. Diagnostic Cytopathology, 41, 174-178(2013).

    [4] Li X, Shi Z Y, Yang Z M et al. Value about artificial intelligence-assisted liquid-based thin-layer cytology for cytology cervical cancer screening[J]. Journal of Capital Medical University, 41, 360-363(2020).

    [5] Chen Y F, Huang P C, Lin K C et al. Semi-automatic segmentation and classification of pap smear cells[J]. IEEE Journal of Biomedical and Health Informatics, 18, 94-108(2014).

    [6] William W, Ware A, Basaza-Ejiri A H et al. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images[J]. Computer Methods and Programs in Biomedicine, 164, 15-22(2018).

    [7] 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], 3144-3148(2018).

    [8] Talo M. Diagnostic classification of cervical cell images from pap smear slides[J]. Academic Perspective Procedia, 2, 1043-1050(2019).

    [9] Huang G, Liu Z, Van Der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

    [10] Win K P, Kitjaidure Y, Hamamoto K et al. Computer-assisted screening for cervical cancer using digital image processing of pap smear images[J]. Applied Sciences, 10, 1800(2020).

    [11] Du , Li X Y, Li Q H. Detection and classification of cervical exfoliated cells based on faster R-CNN[C], 52-57(2019).

    [12] Liang Y X, Pan C L, Sun W X et al. Global context-aware cervical cell detection with soft scale anchor matching[J]. Computer Methods and Programs in Biomedicine, 204, 106061(2021).

    [13] Liang Y X, Tang Z H, Yan M et al. Comparison detector for cervical cell/clumps detection in the limited data scenario[J]. Neurocomputing, 437, 195-205(2021).

    [14] Xin Z H, Lei J Q, Guo C et al. Research progresses of deep learning in diagnosis and treatment of cervical cancer[J]. Chinese Journal of Medical Imaging Technology, 38, 779-782(2022).

    [16] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2022).

    [17] Pan X R, Ge C J, Lu R et al. On the integration of self-attention and convolution[C], 805-815(2022).

    [18] Strudel R, Garcia R, Laptev I et al. Segmenter: transformer for semantic segmentation[C], 7242-7252(2022).

    [19] Hadsell R, Chopra S, LeCun Y. Dimensionality reduction by learning an invariant mapping[C], 1735-1742(2006).

    [20] Li Y H, Mao H Z, Girshick R, Avidan S, Brostow G, Cissé M et al. Exploring plain vision transformer backbones for object detection[M]. Computer vision-ECCV 2022. Lecture notes in computer science, 13669, 280-296(2022).

    [21] Pan X R, Xia Z F, Song S J et al. 3D object detection with pointformer[C], 7459-7468(2021).

    [22] Wang W H, Xie E Z, Li X et al. Pyramid vision transformer: a versatile backbone for dense prediction without convolutions[C], 548-558(2022).

    [23] Liu W L, Li C, Xu N et al. CVM-Cervix: a hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron[J]. Pattern Recognition, 130, 108829(2022).

    [24] Liu Y T, Zhao J J, Luo Q Y et al. Automated classification of cervical lymph-node-level from ultrasound using Depthwise Separable Convolutional Swin Transformer[J]. Computers in Biology and Medicine, 148, 105821(2022).

    [25] Girshick R. Fast R-CNN[C], 1440-1448(2016).

    [27] Liang Y Q, Zhao S Q, Wang H T et al. Two-stage detection method for abnormal cluster cervical cells[J]. Journal of Harbin University of Science and Technology, 27, 76-84(2022).

    [28] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C], 2999-3007(2017).

    [29] Carion N, Massa F, Synnaeve G, Vedaldi A, Bischof H, Brox T et al. End-to-end object detection with transformers[M]. Computer vision–ECCV 2020. Lecture notes in computer science, 12346, 213-229(2020).

    [30] Tian Z, Shen C H, Chen H et al. FCOS: fully convolutional one-stage object detection[C], 9626-9635(2020).

    [32] Sun P Z, Zhang R F, Jiang Y et al. Sparse R-CNN: end-to-end object detection with learnable proposals[C], 14449-14458(2021).

    [33] Cai Z W, Vasconcelos N. Cascade R-CNN: delving into high quality object detection[C], 6154-6162(2018).

    [34] Zheng A L, Zhang Y A, Zhang X Y et al. Progressive end-to-end object detection in crowded scenes[C], 847-856(2022).

    [35] Cao L, Yang J Y, Rong Z W et al. A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening[J]. Medical Image Analysis, 73, 102197(2021).

    [36] Selvaraju R R, Cogswell M, Das A et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C], 618-626(2017).

    Tools

    Get Citation

    Copy Citation Text

    Zheng Zhang, Mingxiao Chen, Xinyu Li, Yi Chen, Shuwei Shen, Peng Yao. Automatic Identification of Cervical Abnormal Cells Based on Transformer[J]. Chinese Journal of Lasers, 2024, 51(3): 0307108

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Biomedical Optical Imaging

    Received: Oct. 9, 2023

    Accepted: Dec. 1, 2023

    Published Online: Feb. 19, 2024

    The Author Email: Yao Peng (yaopeng@ustc.edu.cn)

    DOI:10.3788/CJL231261

    CSTR:32183.14.CJL231261

    Topics