Unveiling Fairness Biases in Deep Learning-Based Brain MRI
  Reconstruction
Yuning Du Yuyang Xue Rohan Dharmakumar Sotirios A. Tsaftaris
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.