The DOI matches its Crossref record; 58 of the 65 references it cites were found in a registry or an index.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
Open accessAbstract
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.
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“Nevertheless, using federated learning, Yan et al. have demonstrated the possibility of training self-supervised models with data from multiple healthcare centers without the need for explicitly sharing data, and have shown improvement in robustness and performance over models trained using data from only one institution97.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [97]
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Cites
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Cited by
Sorted by citations
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518
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Self-citationEmerging trends in federated learning: from model fusion to federated X learning
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Metadata
- Authors
- Yan, R., Qu, L., Wei, Q., Huang, S.-C., Shen, L., Rubin, D. L., Xing, L., & Zhou, Y.
- Affiliations
- Stanford University (US)
- University of Hong Kong (HK)
- Stanford Medicine (US)
- University of California, Santa Cruz (US)
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2023-07
- Volume
- 42
- Issue
- 7
- Pages
- 1932-1943
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC10880587
- OpenAlex
- W4280619489
- Cited by
- 183
- References
- 65
- Citations by year
- 2026: 38 · 2025: 76 · 2024: 43 · 2023: 24
- FWCI
- 17.06
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Institutes of Health (R01CA256890, R01CA227713, U01CA242879, R01HL155410, R01LM012966)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, self-supervised learning, data heterogeneity, non-IID data, label efficiency, masked image modeling, transformer pre-training, medical image classification, privacy-preserving collaborative learning, out-of-distribution generalization, limited labeled data, retinal image classification, dermatological image classification, chest X-ray classification
- MeSH terms
- Algorithms, Diagnostic Imaging, Radiography, Retina
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