Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

Yan, Rui; Qu, Liangqiong; Wei, Qingyue; Huang, Shih-Cheng; Shen, Liyue; Rubin, Daniel L.; Xing, Lei; Zhou, Yuyin

doi:10.1109/tmi.2022.3233574

Cited by 183Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

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Yan, Rui, Qu, Liangqiong, Wei, Qingyue, Huang, Shih-Cheng, Shen, Liyue, Rubin, Daniel L., Xing, Lei, Zhou, Yuyin·2023·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2022.3233574PMC PMC10880587

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Abstract

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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Yan et al. have demonstrated the possibility of training self-supervised models with data from multiple healthcare centers without explicitly sharing data, and have shown improvement in robustness and performance over models trained using data from only one institution.
“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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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: Yan, Rui
Qu, Liangqiong
Wei, Qingyue
Huang, Shih-Cheng
Shen, Liyue
Rubin, Daniel L.
Xing, Lei
Zhou, Yuyin
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2023-07
Volume: 42
Issue: 7
Pages: 1932-1943
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2022.3233574
PMC: PMC10880587
OpenAlex: W4280619489
Cited by: 183
References: 65
Citations by year: 2026: 38 · 2025: 76 · 2024: 43 · 2023: 24 · 2022: 2
FWCI: 17.06
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

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