Skip to main content

Investigating the Predictive Reproducibility of Federated Graph Neural Networks Using Medical Datasets

  • Conference paper
  • First Online:
Predictive Intelligence in Medicine (PRIME 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13564))

Included in the following conference series:

Abstract

Graph neural networks (GNNs) have achieved extraordinary enhancements in various areas including the fields medical imaging and network neuroscience where they displayed a high accuracy in diagnosing challenging neurological disorders such as autism. In the face of medical data scarcity and high-privacy, training such data-hungry models remains challenging. Federated learning brings an efficient solution to this issue by allowing to train models on multiple datasets, collected independently by different hospitals, in fully data-preserving manner. Although both state-of-the-art GNNs and federated learning techniques focus on boosting classification accuracy, they overlook a critical unsolved problem: investigating the reproducibility of the most discriminative biomarkers (i.e., features) selected by the GNN models within a federated learning paradigm. Quantifying the reproducibility of a predictive medical model against perturbations of training and testing data distributions presents one of the biggest hurdles to overcome in developing translational clinical applications. To the best of our knowledge, this presents the first work investigating the reproducibility of federated GNN models with application to classifying medical imaging and brain connectivity datasets. We evaluated our framework using various GNN models trained on medical imaging and connectomic datasets. More importantly, we showed that federated learning boosts both the accuracy and reproducibility of GNN models in such medical learning tasks. Our source code is available at https://github.com/basiralab/reproducibleFedGNN.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://medmnist.com/.

  2. 2.

    https://github.com/basiralab/RG-Select.

References

  1. Lee, J.G., et al.: Deep learning in medical imaging: general overview. Korean J. Radiol. 18, 570–584 (2017)

    Article  Google Scholar 

  2. Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221 (2017)

    Article  Google Scholar 

  3. Wolterink, J., Suk, J.: Geometric deep learning for precision medicine. Key Enabling Technol. Sci. Mach. Learn. 60

    Google Scholar 

  4. Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond euclidean data. IEEE Signal Process. Mag. 34, 18–42 (2017)

    Article  Google Scholar 

  5. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2020)

    Article  MathSciNet  Google Scholar 

  6. Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience. arXiv preprint arXiv:2106.03535 (2021)

  7. McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, PMLR, pp. 1273–1282 (2017)

    Google Scholar 

  8. Nebli, A., Gharsallaoui, M.A., Gürler, Z., Rekik, I., Initiative, A.D.N., et al.: Quantifying the reproducibility of graph neural networks using multigraph data representation. Neural Netw. 148, 254–265 (2022)

    Article  Google Scholar 

  9. Georges, N., Mhiri, I., Rekik, I., Initiative, A.D.N., et al.: Identifying the best data-driven feature selection method for boosting reproducibility in classification tasks. Pattern Recogn. 101, 107183 (2020)

    Google Scholar 

  10. Georges, N., Rekik, I.: Data-specific feature selection method identification for most reproducible connectomic feature discovery fingerprinting brain states. In: Wu, G., Rekik, I., Schirmer, M.D., Chung, A.W., Munsell, B. (eds.) CNI 2018. LNCS, vol. 11083, pp. 99–106. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00755-3_11

    Chapter  Google Scholar 

  11. Forcier, M.B., Gallois, H., Mullan, S., Joly, Y.: Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers? J. Law Biosci. 6, 317 (2019)

    Article  Google Scholar 

  12. Chen, C., Hu, W., Xu, Z., Zheng, Z.: Fedgl: federated graph learning framework with global self-supervision. arXiv preprint arXiv:2105.03170 (2021)

  13. He, C., et al.: Fedgraphnn: a federated learning benchmark system for graph neural networks. In: ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML) (2021)

    Google Scholar 

  14. Yang, J., et al.: Medmnist v2: A large-scale lightweight benchmark for 2D and 3d biomedical image classification. arXiv preprint arXiv:2110.14795 (2021)

  15. Gereige, R.S., Laufer, P.M.: Pneumonia. Pediatr. Rev. 34, 438–456 (2013)

    Article  Google Scholar 

  16. Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014)

    Article  Google Scholar 

  17. Soussia, M., Rekik, I.: Unsupervised manifold learning using high-order morphological brain networks derived from T1-w MRI for autism diagnosis. Front. Neuroinform. 12, 70 (2018)

    Article  Google Scholar 

  18. Fischl, B., et al.: Sequence-independent segmentation of magnetic resonance images. Neuroimage 23, S69–S84 (2004)

    Article  Google Scholar 

  19. Fischl, B.: Freesurfer. Neuroimage 62, 774–781 (2012)

    Article  Google Scholar 

  20. Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 1–14 (2018)

    Article  Google Scholar 

  21. Ying, R., You, J., Morris, C., Ren, X., Hamilton, W.L., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. arXiv preprint arXiv:1806.08804 (2018)

  22. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  23. Lou, B., et al.: Quantitative analysis of synthetic magnetic resonance imaging in Alzheimer’s disease. Front. Aging Neurosci. 13, 638731 (2021)

    Google Scholar 

  24. Gasquoine, P.G.: Contributions of the insula to cognition and emotion. Neuropsychol. Rev. 24, 77–87 (2014). https://doi.org/10.1007/s11065-014-9246-9

    Article  Google Scholar 

  25. Nomi, J.S., Molnar-Szakacs, I., Uddin, L.Q.: Insular function in autism: update and future directions in neuroimaging and interventions. Prog. Neuropsychopharmacol. Biol. Psychiatry 89, 412–426 (2019)

    Article  Google Scholar 

  26. Gebauer, L., Foster, N.E., Vuust, P., Hyde, K.L.: Is there a bit of autism in all of us? autism spectrum traits are related to cortical thickness differences in both autism and typical development. Res. Autism Spectr. Disord. 13, 8–14 (2015)

    Article  Google Scholar 

  27. Habata, K., et al.: Relationship between sensory characteristics and cortical thickness/volume in autism spectrum disorders. Transl. Psychiatry 11, 1–7 (2021)

    Article  Google Scholar 

  28. Kitamura, S., et al.: Association of adverse childhood experiences and precuneus volume with intrusive reexperiencing in autism spectrum disorder. Autism Res. 14, 1886–1895 (2021)

    Article  Google Scholar 

  29. Khundrakpam, B.S., Lewis, J.D., Kostopoulos, P., Carbonell, F., Evans, A.C.: Cortical thickness abnormalities in autism spectrum disorders through late childhood, adolescence, and adulthood: a large-scale MRI study. Cereb. Cortex 27, 1721–1731 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by generous grants from the European H2020 Marie Sklodowska-Curie action (grant no. 101003403, http://basira-lab.com/normnets/) to I.R. and the Scientific and Technological Research Council of Turkey to I.R. under the TUBITAK 2232 Fellowship for Outstanding Researchers (no. 118C288, http://basira-lab.com/reprime/). However, all scientific contributions made in this project are owned and approved solely by the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Islem Rekik .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 635 KB)

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

Balık, M.Y., Rekik, A., Rekik, I. (2022). Investigating the Predictive Reproducibility of Federated Graph Neural Networks Using Medical Datasets. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C. (eds) Predictive Intelligence in Medicine. PRIME 2022. Lecture Notes in Computer Science, vol 13564. Springer, Cham. https://doi.org/10.1007/978-3-031-16919-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16919-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16918-2

  • Online ISBN: 978-3-031-16919-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics