What question did this study set out to answer?

The aim is to enhance fairness in medical image segmentation across demographic groups by utilizing curriculum learning and federated learning techniques.

April 21, 2026Open Access

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Key Points

The aim is to enhance fairness in medical image segmentation across demographic groups by utilizing curriculum learning and federated learning techniques.
Implemented curriculum learning and progressive loss strategies in deep learning models.
Evaluated performance in both centralized and federated learning settings on hip and knee radiographs.
Developed novel fairness-oriented federated algorithms and assessed using statistical significance tests.
Demonstrated consistently improved segmentation accuracy and reduced performance disparities across demographics.
FedIoUoutlier achieved the highest mean Intersection over Union while reducing skewed error and disparity.
Federated models matched or exceeded centralized model performance with significant per-sample IoU improvements.

Abstract

Background Bias 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. Methods This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t -tests to compare per-sample IoU performance against baseline configurations. Results Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. Conclusion The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

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