Abstract
Accurately classifying cervical cells based on the commonly used TBS (The Bethesda System) standard is critical for building the automatic cytology diagnosing system. However, the existing two publicly available datasets (Herlev and SIPaKMeD) do not provide the TBS labels for the cervical cells, leading to the lack of practicability of the models trained based on them. In addition, most deep learning-based methods for cervical cell classification do not explore the attention spots of the model, resulting in the limit of further focusing on discriminative parts of tiny cells. In this paper, we first build a cervical cell image dataset consisting of 10,294 cell image patches with TBS labels from 238 specimens, named LTCDD (Landing TBS Cervical Cell Dataset). Then we propose a novel visual attention module, channel spatial collaborative attention (CSCA), based on the integration of the channel and spatial attention. Two kinds of comparing experiments on LTCDD and two public datasets show that the CSCA embedded network can achieve the best classification performance. Moreover, the CSCA module performs better than other attention modules when integrated into ResNet. The visualization results illustrate that CSCA embedded networks can pay more attention to the nucleus part of cell images so as to focus on learning discriminative features that are beneficial to the classification of the cells.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021)
Elsheikh, T.M., Austin, R.M., Chhieng, D.F., Miller, F.S., Moriarty, A.T., Renshaw, A.A.: 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. Diagn. Cytopathol. 41(2), 174–178 (2013)
William, W., Ware, A., Basaza-Ejiri, A.H., Obungoloch, J.: A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Comput. Methods Programs Biomed. 164, 15–22 (2018)
Chen, Y.F., et al.: Semi-automatic segmentation and classification of pap smear cells. IEEE J. Biomed. Health Inform. 18(1), 94–108 (2013)
Song, Y., et al.: Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Trans. Med. Imaging 36(1), 288–300 (2016)
Wan, T., Xu, S., Sang, C., Jin, Y., Qin, Z.: Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks. Neurocomputing 365, 157–170 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Zhang, L., Lu, L., Nogues, I., Summers, R.M., Liu, S., Yao, J.: DeepPap: deep convolutional networks for cervical cell classification. IEEE J. Biomed. Health Inform. 21(6), 1633–1643 (2017)
Dong, N., Zhao, L., Wu, C.H., Chang, J.F.: Inception v3 based cervical cell classification combined with artificially extracted features. Appl. Soft Comput. 93, 106311 (2020)
Chen, H., et al.: CytoBrain: cervical cancer screening system based on deep learning technology. J. Comput. Sci. Technol. 36(2), 347–360 (2021)
Sabeena, K., Gopakumar, C.: A hybrid model for efficient cervical cell classification. Biomed. Signal Process. Control 72, 103288 (2022)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Zhang, J., Xie, Y., Xia, Y., Shen, C.: Attention residual learning for skin lesion classification. IEEE Trans. Med. Imaging 38(9), 2092–2103 (2019)
He, X., Deng, Y., Fang, L., Peng, Q.: Multi-modal retinal image classification with modality-specific attention network. IEEE Trans. Med. Imaging 40(6), 1591–1602 (2021)
Marinakis, Y., Dounias, G., Jantzen, J.: Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Comput. Biol. Med. 39(1), 69–78 (2009)
Plissiti, M.E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., Charchanti, A.: Sipakmed: a new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3144–3148. IEEE (2018)
Nayar, R., Wilbur, D.C.: The Bethesda system for reporting cervical cytology: a historical perspective. Acta Cytol. 61(4–5), 359–372 (2017)
Reyes, M.C., Cooper, K., et al.: Cervical cancer biopsy reporting: a review. Indian J. Pathol. Microbiol. 57(3), 364 (2014)
Nayar, R., Wilbur, D.C. (eds.): Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11074-5
Xie, S., Girshick, R., Doll’ar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 128(2), 336–359 (2020)
Acknowledgements
This work was supported by the Major Projects of Technological Innovation in Hubei Province (2019AEA170), the Frontier Projects of Wuhan for Application Foundation (2019010701011381), the Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University (ZNJC202226).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, P., Liu, J., Chen, H., Li, C., Pang, B., Cao, D. (2023). Channel Spatial Collaborative Attention Network for Fine-Grained Classification of Cervical Cells. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_45
Download citation
DOI: https://doi.org/10.1007/978-981-99-1645-0_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1644-3
Online ISBN: 978-981-99-1645-0
eBook Packages: Computer ScienceComputer Science (R0)