Skip to main content

Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer

  • Conference paper
  • First Online:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Abstract

PURPOSE: The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first time, we propose Evidential Graph Transformer network, a combination of attention mapping and uncertainty estimation to increase the performance and interpretability of surgical margin assessment. METHODS: The Evidential Graph Transformer was formulated to output the uncertainty estimation along with intermediate attentions. The performance of the model was compared with different baselines in an ex-vivo cross-validation scheme, with extensive ablation study. The association of the model with clinical features were explored. The model was further validated for a prospective ex-vivo data, as well as a breast conserving surgery intra-operative data. RESULTS: The purposed model outperformed all baselines, statistically significantly, with average balanced accuracy of 91.6%. When applied to intra-operative data, the purposed model improved the false positive rate of the baselines. The estimated attention distribution for status of different hormone receptors agreed with reported metabolic findings in the literature. CONCLUSION: Deployment of ex-vivo models is challenging due to the tissue heterogeneity of intra-operative data. The proposed Evidential Graph Transformer is a powerful tool that while providing the attention distribution of biochemical subbands, improve the surgical deployment power by providing decision confidence.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Ahmedt-Aristizabal, D., Armin, M.A., Denman, S., Fookes, C., Petersson, L.: Graph-based deep learning for medical diagnosis and analysis: past, present and future. Sensors 21(14), 4758 (2021). https://doi.org/10.3390/S21144758

  2. Akbarifar, F., et al.: Graph-based analysis of mass spectrometry data for tissue characterization with application in basal cell carcinoma surgery. In: SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11598 (2021)

    Google Scholar 

  3. Balog, J., et al.: In vivo endoscopic tissue identification by rapid evaporative ionization mass spectrometry (REIMS). Angewandte Chemie Int. Ed. 54(38), 11059–11062 (2015). https://doi.org/10.1002/anie.201502770

  4. Budczies, J., et al.: Glutamate enrichment as new diagnostic opportunity in breast cancer. Int. J. Cancer 136(7), 1619–1628 (2015). https://doi.org/10.1002/ijc.29152

  5. Demas, D.M., et al.: Glutamine metabolism drives growth in advanced hormone receptor positive breast cancer. Front. Oncol. 9 (2019). https://doi.org/10.3389/fonc.2019.00686

  6. Dunnwald, L.K., Rossing, M.A., Li, C.I.: Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients. Breast Cancer Res. 9(1), R6 (2007). https://doi.org/10.1186/bcr1639

  7. Durasov, N., Bagautdinov, T., Baque, P., Fua, P.: Masksembles for uncertainty estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13539–13548 (2021)

    Google Scholar 

  8. Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. In: Methods and Applications, AAAI Workshop on Deep Learning on Graphs (2021)

    Google Scholar 

  9. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1050–1059. PMLR, New York, New York, USA, 20–22 June 2016

    Google Scholar 

  10. Hargreaves, A.C., Mohamed, M., Audisio, R.A.: Intra-operative guidance: methods for achieving negative margins in breast conserving surgery. J. Surg. Oncol. 110(1), 21–25 (2014). https://doi.org/10.1002/JSO.23645

  11. Jamzad, A., et al.: Graph transformers for characterization and interpretation of surgical margins. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 88–97. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_9

    Chapter  Google Scholar 

  12. Jsang, A.: Subjective Logic: A Formalism for Reasoning Under Uncertainty. Springer, Cham Verlag (2016). https://doi.org/10.1007/978-3-319-42337-1

    Book  Google Scholar 

  13. Kim, S., Kim, D.H., Jung, W.H., Koo, J.S.: Expression of glutamine metabolism-related proteins according to molecular subtype of breast cancer. Endocrine-Related Cancer 20(3), 339–348 (2013). https://doi.org/10.1530/ERC-12-0398

  14. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations. ICLR (2017)

    Google Scholar 

  15. Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  16. Santilli, A., et al.: Domain adaptation and self-supervised learning for surgical margin detection. Int. J. Comput. Assist. Radiol. Surg. 1–9 (2021). https://doi.org/10.1007/s11548-021-02381-6

  17. Santilli, A., et al.: Self-supervised learning for detection of breast cancer in surgical margins with limited data. In: Proceedings - International Symposium on Biomedical Imaging, April 2021, pp. 980–984, April 2021. https://doi.org/10.1109/ISBI48211.2021.9433829

  18. Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  19. Syeda, A.: Self-supervision and uncertainty estimation in surgical margin detection (2023)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amoon Jamzad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Jamzad, A. et al. (2023). Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43990-2_53

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43989-6

  • Online ISBN: 978-3-031-43990-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics