Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis
Hamidreza Moradi
BackgroundBias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments.MethodsThis study investigates fairness-aware medical image segmentation for hip and knee radiographs using