Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
arXiv:2504.08584 · doi:10.1016/j.ejrai.2025.100028
Abstract
Reliable artificial intelligence (AI) models for medical image analysis often depend on large and diverse labeled datasets. Federated learning (FL) offers a decentralized and privacy-preserving approach to training but struggles in highly non-independent and identically distributed (non-IID) settings, where institutions with more representative data may experience degraded performance. Moreover, existing large-scale FL studies have been limited to adult datasets, neglecting the unique challenges posed by pediatric data, which introduces additional non-IID variability. To address these limitations, we analyzed n=398,523 adult chest radiographs from diverse institutions across multiple countries and n=9,125 pediatric images, leveraging transfer learning from general-purpose self-supervised image representations to classify pneumonia and cases with no abnormality. Using state-of-the-art vision transformers, we found that FL improved performance only for smaller adult datasets (P<0.001) but degraded performance for larger datasets (P<0.064) and pediatric cases (P=0.242). However, equipping FL with self-supervised weights significantly enhanced outcomes across pediatric cases (P=0.031) and most adult datasets (P<0.008), except the largest dataset (P=0.052). These findings underscore the potential of easily deployable general-purpose self-supervised image representations to address non-IID challenges in clinical FL applications and highlight their promise for enhancing patient outcomes and advancing pediatric healthcare, where data scarcity and variability remain persistent obstacles.
arXiv commentPublished in European Journal of Radiology Artificial Intelligence
References in corpus (19)
- A Survey on Deep Learning in Medical Image Analysis
- Decoupled Weight Decay Regularization
- A Simple Framework for Contrastive Learning of Visual Representations
- ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
- Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
- PadChest: A large chest x-ray image dataset with multi-label annotated reports
- Deep Learning for Chest X-ray Analysis: A Survey
- iBOT: Image BERT Pre-Training with Online Tokenizer
- Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
- Spreading vectors for similarity search
- Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
- Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
- Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
- Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
- Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation
- Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages
- Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy
- Weighted Ensemble Self-Supervised Learning