Skip to main content

DCCL: A Benchmark for Cervical Cytology Analysis

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11861)


Medical imaging analysis has witnessed impressive progress in recent years thanks to the development of large-scale labeled datasets. However, in many fields, including cervical cytology, a large well-annotated benchmark dataset remains missing. In this paper, we introduce by far the largest cervical cytology dataset, called Deep Cervical Cytological Lesions (referred to as DCCL). DCCL contains 14,432 image patches with around \(1{,}200\times 2{,}000\) pixels cropped from 1,167 whole slide images collected from four medical centers and scanned by one of the three kinds of digital slide scanners. Besides patch level labels, cell level labels are provided, with 27,972 lesion cells labeled based on The 2014 Bethesda System and the bounding box by six board-certified pathologists with eight years of experience on the average. We also use deep learning models to generate the baseline performance for lesion cell detection and cell type classification on DCCL. We believe this dataset can serve as a valuable resource and platform for researchers to develop new algorithms and pipelines for advanced cervical cancer diagnosis and prevention.


  • Cervical cancer screening
  • Liquid-based cytology
  • Deep learning

C. Zhang and D. Liu—Equal contribution.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions



  2. Bora, K., et al.: Pap smear image classification using convolutional neural network. In: 10th ICVGIP, p. 55. ACM (2016)

    Google Scholar 

  3. Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68(6), 394–424 (2018)

    Google Scholar 

  4. Chen, Y.F., et al.: Semi-automatic segmentation and classification of pap smear cells. IEEE J. Biomed. Health Inform. 18, 94–108 (2014)

    CrossRef  Google Scholar 

  5. Everingham, M., et al.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)

    CrossRef  Google Scholar 

  6. He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  7. Huang, G., et al.: Densely connected convolutional networks. In: CVPR (2017)

    Google Scholar 

  8. Jantzen, J., et al.: Pap-smear benchmark data for pattern classification. NiSIS (2005)

    Google Scholar 

  9. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).

    CrossRef  Google Scholar 

  10. Lin, T.-Y., et al.: Focal loss for dense object detection. In: CVPR (2017)

    Google Scholar 

  11. Meiquan, X., et al.: Cervical cytology intelligent diagnosis based on object detection technology (2018)

    Google Scholar 

  12. Nayar, R., et al.: The Pap Test and Bethesda 2014. Acta Cytologica (2015)

    Google Scholar 

  13. Phoulady, H.A., et al.: A new cervical cytology dataset for nucleus detection and image classification (Cervix93) and methods for cervical nucleus detection. arXiv preprint arXiv:1811.09651 (2018)

  14. Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks (2015)

    Google Scholar 

  15. Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Med. Image Anal. 42, 1–13 (2017)

    CrossRef  Google Scholar 

  16. Song, Y., et al.: Automated segmentation of overlapping cytoplasm in cervical smear images via contour fragments. In: AAAI 2018 (2018)

    Google Scholar 

  17. Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: CVPR (2016)

    Google Scholar 

  18. Tucker, J.: CERVISCAN: an image analysis system for experiments in automatic cervical smear prescreening. Comput. Biomed. Res. 9(2), 93–107 (1976)

    CrossRef  Google Scholar 

  19. Zhang, L., et al.: Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining. Cytometry Part A 85, 214–230 (2014)

    CrossRef  Google Scholar 

  20. Zhang, L., et al.: DeepPap: deep convolutional networks for cervical cell classification. IEEE J. Biomed. Health Inform. 21, 1633–1643 (2017)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Pifu Luo or Jiebo Luo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, C. et al. (2019). DCCL: A Benchmark for Cervical Cytology Analysis. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32691-3

  • Online ISBN: 978-3-030-32692-0

  • eBook Packages: Computer ScienceComputer Science (R0)