Improving the fairness and robustness of deep learning classification models through bias mitigation and post hoc calibration

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18 Oct 2026 0 comments

CONCLUSION: This study shows that, although deep learning models achieve similar classification performance, they differ significantly in terms of calibration, stability, and fairness. PMDN provides more consistent and effective age bias mitigation than the CF-Net approach, particularly in clinically relevant subgroups. Post hoc calibration improves probability reliability further, with nonparametric methods achieving the best calibration.