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

Abstract Purpose: The goal of this study was to enhance the fairness and clinical applicability of deep learning models for classifying Alzheimer’s disease from structural MRI scans. To achieve this, we addressed two key challenges: age-related bias and unreliable probability estimates. Specifically, we investigated in-processing debiasing strategies to achieve age-invariant predictions while preserving diagnostic accuracy. We also systematically evaluated post hoc calibration methods to improve the reliability, interpretability, and clinical usefulness of predicted probabilities across diverse patient populations.