Skip to main content
SpringerLink
Log in

Deep anonymization of medical imaging

  • Track 2: Medical Applications of Multimedia
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Deep learning has shown record-shattering performance in multiple medical tasks. However, data quantity and quality are crucial requirements. As a matter of fact, data is one of the most challenging issues while deploying deep learning models for different tasks. One of the main challenges is the institutions’ privacy protocols, in particular in the medical field. Indeed, the metadata is usually excluded from the database provided. Many invisible features in images can help tracing anonymized data. We propose to use deep learning to exclude these traces. This article focuses on Magnetic resonance imaging (MRI) and one of the most important features, the equipment used for acquisition. First, we aim to produce an algorithm able to perform well distinguishing multiple MRI equipment from different brands. To this end, we employ a convolution neural network architecture to work on this medical image classification task. The second part of this paper is dedicated to reconstructing the input MRI using a simple auto-encoder. The latter step is to use the auto-encoder in order to mislead the classifier classifying the MRI equipment.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Bengio Y (2009) Learning deep architectures for AI. Now Publishers Inc

  2. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  3. Bernau D, Grassal P-W, Robl J, Kerschbaum F (2019) Assessing differentially private deep learning with membership inference. arXiv:1912.11328

  4. Chen M, Shi X, Zhang Y, Wu D, Guizani M (2017) Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data

  5. Doersch C (2016) Tutorial on variational autoencoders. arXiv:1606.05908

  6. Finlayson S G, Bowers J D, Ito J, Zittrain J L, Beam A L, Kohane I S (2019) Adversarial attacks on medical machine learning. Science 363 (6433):1287–1289

    Article  Google Scholar 

  7. Gatys L A, Ecker A S, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414–2423

  8. Gillies C E, Taylor D F, Cummings B C, Ansari S, Islim F, Kronick S L, Medlin Jr R P, Ward K R (2020) Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: a novel simulation and solution. J Biomed Inform 110:103528

    Article  Google Scholar 

  9. Gondara L (2016) Medical image denoising using convolutional denoising autoencoders. In: 2016 IEEE 16th International conference on data mining workshops (ICDMW). IEEE, pp 241–246

  10. Hamidian S, Sahiner B, Petrick N, Pezeshk A (2017) 3d convolutional neural network for automatic detection of lung nodules in chest ct. In: Medical imaging 2017: computer-aided diagnosis, vol 10134. International Society for Optics and Photonics, p 1013409

  11. Howard J P, Fisher L, Shun-Shin M J, Keene D, Arnold A D, Ahmad Y, Cook C M, Moon J C, Manisty C H, Whinnett Z I et al (2019) Cardiac rhythm device identification using neural networks. JACC: Clin Electrophysiol 5(5):576–586

    Google Scholar 

  12. Jafari H, Omotere O, Adesina D, Wu H-H, Qian L (2018) Iot devices fingerprinting using deep learning. In: MILCOM 2018-2018 IEEE military communications conference (MILCOM). IEEE, pp 1–9

  13. Jeong Y U, Yoo S, Kim Y-H, Shim W H (2020) De-identification of facial features in magnetic resonance images: software development using deep learning technology. J Med Int Res 22(12):e22739

    Google Scholar 

  14. Jordon J, Jarrett D, Yoon J, Elbers P, Thoral P, Ercole A, Zhang C, Belgrave D, van der Schaar M (2020) Hide-and-seek privacy challenge synthetic data generation vs. patient re-identification with clinical time-series data

  15. Kamnitsas K, Ledig C, Newcombe VF, Simpson J P, Kane A D, Menon D K, Rueckert D, Glocker B (2017) Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med Image Anal 36:61–78

    Article  Google Scholar 

  16. Kharrazi M, Sencar H T, Memon N (2004) Blind source camera identification. In: 2004 International conference on image processing, 2004. ICIP’04, vol 1. IEEE, pp 709–712

  17. Kim T, Yang J (2019) Latent-space-level image anonymization with adversarial protector networks. IEEE Access 7:84992–84999. https://doi.org/10.1109/ACCESS.2019.2924479

    Article  Google Scholar 

  18. Kotak J, Elovici Y (2020) Iot device identification using deep learning. In: Conference on complex, intelligent, and software intensive systems. Springer, pp 76–86

  19. Kuppa A, Aouad L, Le-Khac N-A (2021) Towards improving privacy of synthetic datasets. In: Annual privacy forum. Springer, pp 106–119

  20. Lawrence S, Giles C L, Tsoi A C, Back A D (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neur Netw 8(1):98–113

    Article  Google Scholar 

  21. LeCun Y, Bengio Y, et al. (1995) Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Netw 3361(10):1995

    Google Scholar 

  22. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  23. Li A, Duan Y, Yang H, Chen Y, Yang J (2020) Tiprdc: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 824–832

  24. Livraga G, Paraboschi S First report on privacy metrics and data sanitisation

  25. Lu X, Tsao Y, Matsuda S, Hori C (2013) Speech enhancement based on deep denoising autoencoder. In: Interspeech, vol 2013, pp 436–440

  26. Makhzani A, Frey B (2013) K-sparse autoencoders. arXiv:1312.5663

  27. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial autoencoders. arXiv:1511.05644

  28. Merchant K, Revay S, Stantchev G, Nousain B (2018) Deep learning for rf device fingerprinting in cognitive communication networks. IEEE J Selected Topics Signal Process 12(1):160–167

    Article  Google Scholar 

  29. Pereira S, Pinto A, Alves V, Silva C A (2016) Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans Med Imag 35(5):1240–1251

    Article  Google Scholar 

  30. Price W N, Cohen I G (2019) Privacy in the age of medical big data. Nat Med 25(1):37–43

    Article  Google Scholar 

  31. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the rprop algorithm. In: IEEE international conference on neural networks. IEEE, pp 586–591

  32. Riyaz S, Sankhe K, Ioannidis S, Chowdhury K (2018) Deep learning convolutional neural networks for radio identification. IEEE Commun Mag 56(9):146–152

    Article  Google Scholar 

  33. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  34. Ryu J, Zheng Y, Gao Y, Abuadbba S, Kim J, Won D, Nepal S, Kim H, Wang C (2021) Can differential privacy practically protect collaborative deep learning inference for the internet of things? arXiv–2104

  35. Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification. In: International conference on information processing in medical imaging. Springer, pp 588–599

  36. Vieira S, Pinaya WH, Mechelli A (2017) Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev 74:58–75

    Article  Google Scholar 

  37. Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Advances in Neural Information Processing Systems, 29

  38. Wu Q, Feres C, Kuzmenko D, Zhi D, Yu Z, Liu X, et al. (2018) Deep learning based rf fingerprinting for device identification and wireless security. Electron Lett 54(24):1405–1407

    Article  Google Scholar 

  39. Xie L, Lin K, Wang S, Wang F, Zhou J (2018) Differentially private generative adversarial network

  40. Xu R (2020) Functional encryption based approaches for practical privacy-preserving machine learning. Ph.D. Thesis. University of Pittsburgh

  41. Yoon J, Drumright L N, Van Der Schaar M (2020) Anonymization through data synthesis using generative adversarial networks (ads-gan). IEEE J Biomed Health Inform 24(8):2378–2388

    Article  Google Scholar 

  42. Yu D, Zhang H, Chen W, Liu T-Y (2021) Do not let privacy overbill utility: gradient embedding perturbation for private learning. arXiv–2102

  43. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881–2890

Download references

Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.;Cogstate;Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.;NeuroRx Research; Neurotrack Technologies;Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics.The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Funding

This study was funded by the University of Poitiers doctoral scholarship.

Author information

Authors and Affiliations

  1. XLIM Research Institute, UMR CNRS 7252 University of Poitiers, Poitiers, France

    Lobna Fezai, Thierry Urruty, Pascal Bourdon & Chrsitine Fernandez-Maloigne

  2. I3M, Common Laboratory CNRS-Siemens University and Hospital of Poitiers, Poitiers, France

    Lobna Fezai, Thierry Urruty, Pascal Bourdon & Chrsitine Fernandez-Maloigne

Authors
  1. Lobna Fezai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thierry Urruty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pascal Bourdon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chrsitine Fernandez-Maloigne
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

for the Alzheimer’s Disease Neuroimaging Initiative

Corresponding author

Correspondence to Lobna Fezai.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fezai, L., Urruty, T., Bourdon, P. et al. Deep anonymization of medical imaging. Multimed Tools Appl 82, 9533–9547 (2023). https://doi.org/10.1007/s11042-022-13686-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13686-2

Keywords

Search

Navigation