Chinese Journal of Lasers, Volume. 51, Issue 3, 0307108(2024)
Automatic Identification of Cervical Abnormal Cells Based on Transformer
[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).
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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
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)
CSTR:32183.14.CJL231261