ArticleScientific reports2023
Collaborative training of medical artificial intelligence models with non-uniform labels.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Resolution-dependent self-supervised transfer in chest radiograph classification.Communications medicine · 2026Article
- Artificial intelligence in periodontal disease research: a bibliometric and visualized analysis of global research trends (2007-2025).The Saudi dental journal · 2026Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.NPJ digital medicine · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Advancing breast, lung and prostate cancer research with federated learning. A systematic review.NPJ digital medicine · 2025Article
- Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence.Sensors (Basel, Switzerland) · 2024Article
- Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Review
- Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging.Communications medicine · 2024Article
- Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images.European radiology experimental · 2024Article
- Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled vary widely. For Instance, an institution might provide a dataset of chest radiographs containing labels denoting the presence of pneumonia, while another institution might have a focus on determining the presence of metastases in the lung. Training a single AI model utilizing all these data is not feasible with conventional federated learning (FL). This prompts us to propose an extension to the widespread FL process, namely flexible federated learning (FFL) for collaborative training on such data. Using 695,000 chest radiographs from five institutions from across the globe-each with differing labels-we demonstrate that having heterogeneously labeled datasets, FFL-based training leads to significant performance increase compared to conventional FL training, where only the uniformly annotated images are utilized. We believe that our proposed algorithm could accelerate the process of bringing collaborative training methods from research and simulation phase to the real-world applications in healthcare.
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