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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)


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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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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Correspondence to Yuning Du .

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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.

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