Skip to main content

WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histopathological Whole-Slide Images

  • Conference paper
  • First Online:
Machine Learning in Medical Imaging (MLMI 2019)

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

Included in the following conference series:


This paper proposes a novel network WSI-Net for segmentation and classification of gigapixel breast whole-slide images (WSIs). WSI-Net can segment patches from the WSI into three types, including non-malignant, ductal carcinoma in situ, and invasive ductal carcinoma. It adds a parallel classification branch on the top of the low layer of a semantic segmentation model DeepLab. This branch can fast identify and discard those non-malignant patches in advance and thus the high layer of DeepLab can only focus on the remaining possible cancerous inputs. This strategy can accelerate inference and robustly improve segmentation performance. For training WSI-Net, a hierarchy-aware loss function is proposed to combine pixel-level and patch-level loss, which can capture the pathology hierarchical relationships between pixels in each patch. By aggregating patch segmentation results from WSI-Net, we generate a segmentation map for the WSI and extract its morphological features for WSI-level classification. Experimental results show that our WSI-Net can be fast, robust and effective on our benchmark dataset.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Institutional subscriptions

Similar content being viewed by others


  1. 1.

    “Primitive score” means the result on the initial test dataset without resampling.


  1. Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama 318(22), 2199–2210 (2017)

    Article  Google Scholar 

  2. Bejnordi, B.E., et al.: Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. J. Med. Imaging 4(4), 044504 (2017)

    Article  Google Scholar 

  3. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    Article  Google Scholar 

  4. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)

    Google Scholar 

  5. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014).

    Chapter  Google Scholar 

  6. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

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

  8. Mehta, S., Mercan, E., Bartlett, J., Weave, D., Elmore, J.G., Shapiro, L.: Y-net: joint segmentation and classification for diagnosis of breast biopsy images. arXiv preprint arXiv:1806.01313 (2018)

  9. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  10. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).

    Chapter  Google Scholar 

  11. Wu, Y., He, K.: Group normalization. arXiv preprint arXiv:1803.08494 (2018)

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Hong Liu .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 6854 KB)

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

Ni, H., Liu, H., Wang, K., Wang, X., Zhou, X., Qian, Y. (2019). WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histopathological Whole-Slide Images. 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)

Publish with us

Policies and ethics