Label-free AI bias mitigation

Traditional bias correction methods in medical imaging, whether through post-training adjustments, data pre-processing, or in-training techniques, typically rely on labeled information such as age, gender, or ethnicity. This reliance may lead to overlooking subtle biases that arise from less obvious data variations or demographic factors and fails to utilize clinically relevant information that may not be labeled. Our proposed approach addresses these gaps by integrating optimal transport (OT)-based bias mitigation directly into the AI model training process without the requirement of labeled information. This method measures and aligns feature distributions within a class by leveraging their inherent similarities, capturing nuanced discrepancies that conventional approaches might miss.