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Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction

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Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (CLIP 2023, EPIMI 2023, FAIMI 2023)

Abstract

Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. In neuroimaging, DL methods can reconstruct high-quality images from undersampled data. However, it is essential to consider fairness in DL algorithms, particularly in terms of demographic characteristics. This study presents the first fairness analysis in a DL-based brain MRI reconstruction model. The model utilises the U-Net architecture for image reconstruction and explores the presence and sources of unfairness by implementing baseline Empirical Risk Minimisation (ERM) and rebalancing strategies. Model performance is evaluated using image reconstruction metrics. Our findings reveal statistically significant performance biases between the gender and age subgroups. Surprisingly, data imbalance and training discrimination are not the main sources of bias. This analysis provides insights of fairness in DL-based image reconstruction and aims to improve equity in medical AI applications.

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Notes

  1. 1.

    https://github.com/facebookresearch/fastMRI/.

References

  1. Gunning-Dixon, F.M., Brickman, A.M., Cheng, J.C., Alexopoulos, G.S.: Aging of cerebral white matter: a review of MRI findings. Int. J. Geriatric Psychiatry: J. Psychiatry Late Life Allied Sci. 24(2), 109–117 (2009)

    Article  Google Scholar 

  2. Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)

    Google Scholar 

  3. Hellman, D.: When is Discrimination Wrong? Harvard University Press, Cambridge (2008)

    Google Scholar 

  4. Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1–33 (2012)

    Article  Google Scholar 

  5. Lin, D.J., Johnson, P.M., Knoll, F., Lui, Y.W.: Artificial intelligence for MR image reconstruction: an overview for clinicians. J. Magn. Reson. Imaging 53(4), 1015–1028 (2021)

    Article  Google Scholar 

  6. Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (oasis): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)

    Article  Google Scholar 

  7. Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., Weinberger, K.Q.: On fairness and calibration. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  8. Puyol-Antón, E., et al.: Fairness in cardiac magnetic resonance imaging: assessing sex and racial bias in deep learning-based segmentation. Front. Cardiovasc. Med. 9, 859310 (2022)

    Article  Google Scholar 

  9. Puyol-Antón, E., et al.: Fairness in Cardiac MR image analysis: an investigation of bias due to data imbalance in deep learning based segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 413–423. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_39

    Chapter  Google Scholar 

  10. Raisi-Estabragh, Z., Harvey, N.C., Neubauer, S., Petersen, S.E.: Cardiovascular magnetic resonance imaging in the UK biobank: a major international health research resource. Eur. Heart J.-Cardiovasc. Imaging 22(3), 251–258 (2021)

    Article  Google Scholar 

  11. Ritchie, S.J., et al.: Sex differences in the adult human brain: evidence from 5216 UK biobank participants. Cereb. Cortex 28(8), 2959–2975 (2018)

    Article  Google Scholar 

  12. 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). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  13. Slijepcevic, D., et al.: Explaining machine learning models for age classification in human gait analysis. Gait Posture 97, S252–S253 (2022)

    Article  Google Scholar 

  14. Vapnik, V.: Principles of risk minimization for learning theory. In: Advances in Neural Information Processing Systems, vol. 4 (1991)

    Google Scholar 

  15. Winkler, J.K., et al.: Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 155(10), 1135–1141 (2019)

    Article  Google Scholar 

  16. Zhang, H., Dullerud, N., Roth, K., Oakden-Rayner, L., Pfohl, S., Ghassemi, M.: Improving the fairness of chest x-ray classifiers. In: Conference on Health, Inference, and Learning, pp. 204–233. PMLR (2022)

    Google Scholar 

  17. Zong, Y., Yang, Y., Hospedales, T.: Medfair: benchmarking fairness for medical imaging. arXiv preprint arXiv:2210.01725 (2022)

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Acknowledgements

This work was supported in part by National Institutes of Health (NIH) grant 7R01HL148788-03. Y. Du and Y. Xue thank additional financial support from the School of Engineering, the University of Edinburgh. S.A. Tsaftaris also acknowledges the support of Canon Medical and the Royal Academy of Engineering and the Research Chairs and Senior Research Fellowships scheme (grant RCSRF1819\(\backslash \) 8\(\backslash \) 25), and the UK’s Engineering and Physical Sciences Research Council (EPSRC) support via grant EP/X017680/1. The authors would like to thank Dr. Chen and K. Vilouras for inspirational discussions and assistance. Data used in Sect. 4.1 were provided by OASIS-1: Cross-Sectional: Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382.

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Du, Y., Xue, Y., Dharmakumar, R., Tsaftaris, S.A. (2023). Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction. In: Wesarg, S., et al. Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging. CLIP EPIMI FAIMI 2023 2023 2023. Lecture Notes in Computer Science, vol 14242. Springer, Cham. https://doi.org/10.1007/978-3-031-45249-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-45249-9_10

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