Skip to main content
SpringerLink
Log in

TriResNet: A Deep Triple-Stream Residual Network for Histopathology Grading

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11663))

Included in the following conference series:

Abstract

While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent manner. In this work, we investigate and explore a deep triple-stream residual network (TriResNet) architecture for the purpose of tile-level histopathology grading, which is the critical first step to computer-aided whole-slide histopathology grading. In particular, the design mentality behind the proposed TriResNet network architecture is to facilitate for the learning of a more diverse set of quantitative features to better characterize the complex tissue characteristics found in histopathology samples. Experimental results on two widely-used computer-aided histopathology benchmark datasets (CAMELYON16 dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the proposed TriResNet network architecture was able to achieve noticeably improved accuracies when compared with two other state-of-the-art deep convolutional neural network architectures for histopathology grading. Based on these promising results, the hope is that the proposed TriResNet network architecture could become a useful tool to aiding pathologists increase the consistency, speed, and accuracy of the histopathology grading process.

The authors thank the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs Program, and the Queen Elizabeth II Graduate Scholarship in Science & Technology for partially supporting this research. Furthermore, the authors thank Nvidia for the GPUs used in this study that were provided as part of a hardware grant.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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. Camelyon (2016). https://camelyon16.grand-challenge.org/. Accessed: 2018–02-01

  2. Allende, D., et al.: Inter-observer and intra-observer variability in the diagnosis of dysplasia in patients with inflammatory bowel disease: correlation of pathological and endoscopic findings. Colorectal Dis. 16(9), 710–718 (2014)

    Article  Google Scholar 

  3. Altunbay, D., Cigir, C., Sokmensuer, C., Gunduz-Demir, C.: Color graphs for automated cancer diagnosis and grading. IEEE Trans. Biomed. Eng. 57(3), 665–674 (2010)

    Article  Google Scholar 

  4. Basavanhally, A., et al.: Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides. IEEE Trans. Biomed. Eng. 60(8), 2089–2099 (2013)

    Article  Google Scholar 

  5. Benediktsson, H., Whitelaw, J., Roy, I.: Pathology services in developing countries: a challenge. Arch. Pathol. Lab. Med. 131(11), 1636–1639 (2007)

    Google Scholar 

  6. Cruz-Roa, A., et al.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: Medical Imaging 2014: Digital Pathology, vol. 9041, p. 904103. International Society for Optics and Photonics (2014)

    Google Scholar 

  7. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)

    Google Scholar 

  8. Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 496–499. IEEE (2008)

    Google Scholar 

  9. Elmore, J.G., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. Jama 313(11), 1122–1132 (2015)

    Article  Google Scholar 

  10. Fischer, A.H., Jacobson, K.A., Rose, J., Zeller, R.: Hematoxylin and eosin staining of tissue and cell sections. Cold Spring Harb. Protoc. 2008(5), pdb-prot4986 (2008)

    Google Scholar 

  11. Goode, A., Gilbert, B., Harkes, J., Jukic, D., Satyanarayanan, M.: OpenSlide: a vendor-neutral software foundation for digital pathology (2013). https://doi.org/10.4103/2153-3539.119005. http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=1;spage=27;epage=27;aulast=Goode;t=6

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Comput. Methods Program. Biomed. 107(3), 538–556 (2012)

    Article  Google Scholar 

  14. Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7 (2016)

    Article  Google Scholar 

  15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  16. Kornblith, S., Shlens, J., Le, Q.V.: Do better ImageNet models transfer better? arXiv preprint arXiv:1805.08974 (2018)

  17. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc., New York (2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

    Google Scholar 

  18. Kumar, V., Abbas, A.K., Fausto, N., Aster, J.C.: Robbins and Cotran Pathologic Basis of Disease. Elsevier Health Sciences, Amsterdam (2014). Professional Edition e-book

    Google Scholar 

  19. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  20. Lee, B., Paeng, K.: Breast cancer stage classification in histopathology images (2017)

    Google Scholar 

  21. Li, Y., Ping, W.: Cancer metastasis detection with neural conditional random field. In: International conference on Medical Imaging with Deep Learning (2018)

    Google Scholar 

  22. Liu, Y., et al.: Detecting cancer metastases on gigapixel pathology images. arXiv preprint arXiv:1703.02442 (2017)

  23. Monaco, J.P., et al.: High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models. Med. Image Anal. 14(4), 617–629 (2010)

    Article  Google Scholar 

  24. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  25. Paszke, A., et al.: Automatic differentiation in PyTorch (2017)

    Google Scholar 

  26. Petushi, S., Garcia, F.U., Haber, M.M., Katsinis, C., Tozeren, A.: Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Med. Imaging 6(1), 14 (2006)

    Article  Google Scholar 

  27. Raab, S.S., et al.: Clinical impact and frequency of anatomic pathology errors in cancer diagnoses. Cancer 104(10), 2205–2213 (2005)

    Article  Google Scholar 

  28. Shah, M., Wang, D., Rubadue, C., Suster, D., Beck, A.H.: Deep learning assessment of tumor proliferation in breast cancer histological images (2017)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. Vang, Y.S., Chen, Z., Xie, X.: Deep learning framework for multi-class breast cancer histology image classification. CoRR abs/1802.00931 (2018). http://arxiv.org/abs/1802.00931

    Google Scholar 

  31. Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)

Download references

Author information

Authors and Affiliations

  1. Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON, Canada

    Rene Bidart & Alexander Wong

  2. Vision and Image Processing Research Group, University of Waterloo, Waterloo, ON, Canada

    Rene Bidart & Alexander Wong

Authors
  1. Rene Bidart
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Wong .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. University of Waterloo, Waterloo, ON, Canada

    Alfred Yu

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

Bidart, R., Wong, A. (2019). TriResNet: A Deep Triple-Stream Residual Network for Histopathology Grading. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27272-2_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27271-5

  • Online ISBN: 978-3-030-27272-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation