Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

  • Conference paper
  • First Online:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13432))

Abstract

Image representation learning has been a key challenge to promote the performance of the histopathological whole slide images analysis. The previous representation learning methods followed the supervised learning paradigm. However, manual annotation for large-scale WSIs is time-consuming and labor-intensive. Hence, the self-supervised contrastive learning has recently attracted intensive attention. In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis. We built a lesion queue based on the memory bank structure to store the representations of different classes of WSIs, which allowed the contrastive model to selectively define the negative pairs during the training. Moreover, We designed a queue refinement strategy to purify the representations stored in the lesion queue. The experimental results demonstrate that LACL achieves the best performance in histopathology image representation learning on different datasets, and outperforms state-of-the-art methods under different WSI classification benchmarks. The code is available at https://github.com/junl21/lacl.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
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

References

  1. Arvaniti, E., et al.: Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci. Rep. 8(1), 1–11 (2018)

    Article  Google Scholar 

  2. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  3. Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)

  4. Chen, X., He, K.: Exploring simple Siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)

    Google Scholar 

  5. Han, L., Murphy, R.F., Ramanan, D.: Learning generative models of tissue organization with supervised GANs. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 682–690. IEEE (2018)

    Google Scholar 

  6. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)

    Google Scholar 

  7. Huang, Z., Chai, H., Wang, R., Wang, H., Yang, Y., Wu, H.: Integration of patch features through self-supervised learning and transformer for survival analysis on whole slide images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 561–570. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_54

    Chapter  Google Scholar 

  8. Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136. PMLR (2018)

    Google Scholar 

  9. Jiang, Y., Chen, L., Zhang, H., Xiao, X.: Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE 14(3), e0214587 (2019)

    Article  Google Scholar 

  10. Lerousseau, M., et al.: Weakly supervised multiple instance learning histopathological tumor segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 470–479. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_45

    Chapter  Google Scholar 

  11. Liu, Q., et al.: SimTriplet: simple triplet representation learning with a single GPU. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 102–112. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_10

    Chapter  Google Scholar 

  12. Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555–570 (2021)

    Article  Google Scholar 

  13. Robinson, J., Chuang, C.Y., Sra, S., Jegelka, S.: Contrastive learning with hard negative samples. arXiv preprint arXiv:2010.04592 (2020)

  14. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  15. Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. In: Advances in Neural Information Processing Systems 34 (2021)

    Google Scholar 

  16. Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., Isola, P.: What makes for good views for contrastive learning? Adv. Neural. Inf. Process. Syst. 33, 6827–6839 (2020)

    Google Scholar 

  17. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  18. Le Vuong, T.T., Kim, K., Song, B., Kwak, J.T.: Ranking loss: a ranking-based deep neural network for colorectal cancer grading in pathology images. In: MICCAI 2021. LNCS, vol. 12908, pp. 540–549. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_52

    Chapter  Google Scholar 

  19. Wang, S., et al.: RMDL: recalibrated multi-instance deep learning for whole slide gastric image classification. Med. Image Anal. 58, 101549 (2019)

    Article  Google Scholar 

  20. Wang, X., et al.: TransPath: transformer-based self-supervised learning for histopathological image classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 186–195. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_18

    Chapter  Google Scholar 

  21. Xiao, T., Wang, X., Efros, A.A., Darrell, T.: What should not be contrastive in contrastive learning. arXiv preprint arXiv:2008.05659 (2020)

  22. Yang, P., Hong, Z., Yin, X., Zhu, C., Jiang, R.: Self-supervised visual representation learning for histopathological images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 47–57. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_5

    Chapter  Google Scholar 

  23. Zheng, M., et al.: Weakly supervised contrastive learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10042–10051 (2021)

    Google Scholar 

  24. Zheng, Y., et al.: Diagnostic regions attention network (DRA-Net) for histopathology WSI recommendation and retrieval. IEEE Trans. Med. Imaging 40(3), 1090–1103 (2020)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partly supported by the National Natural Science Foundation of China [grant no. 61901018, 62171007, 61906058, and 61771031], partly supported by the Anhui Provincial Natural Science Foundation [grant no. 1908085MF210], and partly supported by the Fundamental Research Funds for the Central Universities of China [grant no. JZ2022HGTB0285].

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yushan Zheng or Jun Shi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, J., Zheng, Y., Wu, K., Shi, J., Xie, F., Jiang, Z. (2022). Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16434-7_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16433-0

  • Online ISBN: 978-3-031-16434-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics