Skip to main content
SpringerLink
Log in

A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12132))

Included in the following conference series:

Abstract

Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification. Our novel focus selection step provides fast per-cell focus decisions at human-level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cytology images, improving over previous results both in terms of accuracy and feasibility. The complete source code is made available as open source (https://github.com/MIDA-group/OralScreen).

This work is supported by: Swedish Research Council proj. 2015-05878 and 2017-04385, VINNOVA grant 2017-02447, and FTV Stockholms Län AB.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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

Institutional subscriptions

References

  1. Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301–1309 (2019)

    Article  Google Scholar 

  2. Cruz-Roa, A., et al.: Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017)

    Article  Google Scholar 

  3. Deroulers, C., Ameisen, D., Badoual, M., Gerin, C., Granier, A., Lartaud, M.: Analyzing huge pathology images with open source software. Diagnostic Pathol. 8(1), 92 (2013)

    Article  Google Scholar 

  4. Falk, T., et al.: U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16(1), 67–70 (2019)

    Article  Google Scholar 

  5. Farahani, N., Parwani, A., Pantanowitz, L.: Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Lab Med. Int. 7, 23–33 (2015)

    Google Scholar 

  6. Ferzli, R., Karam, L.: A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans. Image Process. 18(4), 717–728 (2009)

    Article  MathSciNet  Google Scholar 

  7. Girshick, R.: Fast R-CNN. arXiv:1504.08083 [cs], September 2015

  8. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv:1311.2524 [cs], October 2014

  9. Guan, J., Zhang, W., Gu, J., Ren, H.: No-reference blur assessment based on edge modeling. J. Vis. Commun. Image Represent. 29, 1–7 (2015)

    Article  Google Scholar 

  10. Gupta, A., et al.: Deep learning in image cytometry: a review. Cytometry Part A 95(4), 366–380 (2019)

    Article  Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385 [cs], December 2015

  12. Höfener, H., Homeyer, A., Weiss, N., Molin, J., Lundström, C., Hahn, H.: Deep learning nuclei detection: a simple approach can deliver state-of-the-art results. Comput. Med. Imag. Graph. 70, 43–52 (2018)

    Article  Google Scholar 

  13. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. arXiv:1608.06993 [cs], January 2018

  14. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 [cs] (2015)

  15. Jabalee, J., et al.: Identification of malignancy-associated changes in histologically normal tumor-adjacent epithelium of patients with HPV-positive oropharyngeal cancer. Anal. Cellular Pathol. 2018, 1–9 (2018)

    Article  Google Scholar 

  16. Kainz, P., Urschler, M., Schulter, S., Wohlhart, P., Lepetit, V.: You should use regression to detect cells. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 276–283. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_33

    Chapter  Google Scholar 

  17. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs], December 2014

  18. Korbar, B., et al.: Deep learning for classification of colorectal polyps on whole-slide images. J. Pathol. Inform. 8, 30 (2017)

    Article  Google Scholar 

  19. Narvekar, N.D., Karam, L.J.: A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans. Image Process. 20(9), 2678–2683 (2011)

    Article  MathSciNet  Google Scholar 

  20. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of IEEE Conference on CVPR, pp. 779–788 (2016)

    Google Scholar 

  21. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of IEEE Conference on CVPR, pp. 7263–7271 (2017)

    Google Scholar 

  22. Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv:1804.02767 [cs], April 2018

  23. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv:1506.01497 [cs], January 2016

  24. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer Assisted Intervention MICCAI 2015, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  25. Sankaranarayanan, R., et al.: Long term effect of visual screening on oral cancer incidence and mortality in a randomized trial in Kerala. India. Oral Oncol. 49(4), 314–321 (2013)

    Article  Google Scholar 

  26. Speight, P., et al.: Screening for oral cancer—a perspective from the global oral cancer forum. Oral Surg., Oral Med. Oral Pathol. Oral Radiol. 123(6), 680–687 (2017)

    Article  Google Scholar 

  27. Stewart, B., Wild, C.P., et al.: World cancer report 2014. Public Health (2014)

    Google Scholar 

  28. Teramoto, A., et al.: Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network. Inform. Med. Unlocked 16, 100205 (2019)

    Article  Google Scholar 

  29. Us-Krasovec, M., et al.: Malignancy associated changes in epithelial cells of buccal mucosa: a potential cancer detection test. Anal. Quantit. Cytol. Histol. 27(5), 254–262 (2005)

    Google Scholar 

  30. Warnakulasuriya, S.: Global epidemiology of oral and oropharyngeal cancer. Oral Oncol. 45(4–5), 309–316 (2009)

    Article  Google Scholar 

  31. Wetzer, E., Gay, J., Harlin, H., Lindblad, J., Sladoje, N.: When texture matters: texture-focused CNNs outperform general data augmentation and pretraining in oral cancer detection. In: Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) (2020) forthcoming

    Google Scholar 

  32. Wieslander, H., et al.: Deep convolutional neural networks for detecting cellular changes due to malignancy. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 82–89. IEEE, October 2017

    Google Scholar 

  33. Xie, W., Noble, J., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imag. Visual. 6(3), 283–292 (2018)

    Article  Google Scholar 

  34. Xie, Y., Xing, F., Kong, X., Su, H., Yang, L.: Beyond classification: structured regression for robust cell detection using convolutional neural network. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 358–365. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_43

    Chapter  Google Scholar 

  35. 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)

    Article  Google Scholar 

  36. Zou, Z., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. arXiv:1905.05055 [cs], May 2019

Download references

Author information

Authors and Affiliations

  1. Centre for Image Analysis, Department of IT, Uppsala University, Uppsala, Sweden

    Jiahao Lu, Nataša Sladoje & Joakim Lindblad

  2. Department of Orofacial Medicine at Södersjukhuset, Folktandvården Stockholms Län AB, Stockholm, Sweden

    Christina Runow Stark

  3. Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden

    Eva Darai Ramqvist

  4. Department of Surgical sciences, Uppsala University, Uppsala, Sweden

    Jan-Michaél Hirsch

Authors
  1. Jiahao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nataša Sladoje
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christina Runow Stark
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eva Darai Ramqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jan-Michaél Hirsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joakim Lindblad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joakim Lindblad .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lu, J., Sladoje, N., Runow Stark, C., Darai Ramqvist, E., Hirsch, JM., Lindblad, J. (2020). A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50516-5_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50515-8

  • Online ISBN: 978-3-030-50516-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation