Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
Open accessAbstract
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing.
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Federated learning provides an alternative paradigm for accurate and generalizable machine learning by only sharing numerical model updates from decentralized data.
“Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [21]
Federated learning has shown promise in recent medical imaging applications, including COVID-19 analysis.
“FL has already shown promise in recent medical imaging applications40–43, including in COVID-19 analysis8,44,45.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [8]
Seven reviewed papers [21], [24], [25], [27], [28], [31], [32] used data samples between 2,109 and 6,284.
“Next, within the 10,000 sample range, seven papers [21], [24], [25], [27], [28], [31] and [32] used data samples between 2,109 and 6,284 and this number is quite good.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [21]
Only four of the 17 reviewed papers considered non-IID data along with IID data, and the remaining 13 did not discuss it.
“Our results show only four papers (we did not find sufficient explanation from [26] and [21]) considered non-IID type along with IID data and the rest 13 did not talk about the content.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [21]
Yang et al. [21] presented a federated semi-supervised learning method with sufficient experimental details.
“A federated semi-supervised learning”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [21]
In Yang et al. [21], there is a need to compare results with state-of-the-art methods.
“Need to compare results with”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [21]
FL is proven as a promising solution for countries to collaborate in COVID-19 segmentation and detection without worrying about user information leakage and lack of dataset.
“Given the strict regulatory policy on data privacy, FL is proven as a promising solution for countries to collaborate in the COVID-19 segmentation and detection without worrying the user information leakage and lacks of dataset.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [150]
A real-world federated AI project using FL for COVID-19 region segmentation in chest CT with medical institutions from China, Italy, and Japan is presented.
“Recently, a real-world federated AI project using FL for COVID-19 region segmentation in chest CT with the participation of medical institutions from China, Italy, and Japan is presented in [150].”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [150]
Real-world FL project for COVID-19 region segmentation in chest CT with medical institutions from China, Italy, and Japan: FL-based approach captures better ground truth shapes and has less false positives compared to traditional local training.
“Compared with the traditional local training approach using 945 scans with the support of expert radiologists, the FL-based approach is able to capture better the ground truth shapes and has less false positives with the collaboration of training resources from multiple medical centres.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [118]
FL is proven as a promising solution for countries to collaborate in COVID-19 segmentation and detection without worrying user information leakage and lack of dataset.
“Given the strict regulatory policy on data privacy, FL is proven as a promising solution for countries to collaborate in the COVID-19 segmentation and detection without worrying the user information leakage and lacks of dataset.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [118]
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- Authors
- Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., Zhu, W., Carrafiello, G., Patella, F., Cariati, M., Obinata, H., Mori, H., Tamura, K., An, P., Wood, B. J., & Xu, D.
- Affiliations
- Nvidia (United States) (US)
- National Institutes of Health (US)
- University of Milan (IT)
- Ospedale San Paolo (IT)
- Self-Defense Forces Central Hospital (JP)
- Hubei University of Medicine (CN)
- Corresponding author
- Xu, Ziyue
- Xu, Sheng
- Xu, Daguang
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 70
- Pages
- 101992
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC7864789
- OpenAlex
- W3127057363
- Cited by
- 279
- References
- 85
- Citations by year
- 2026: 24 · 2025: 50 · 2024: 49 · 2023: 64 · 2022: 59 · 2021: 32 · 2020: 1
- FWCI
- 26.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Institutes of Health (1ZIACL040015, 1ZIDBC011242)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- federated learning, semi-supervised learning, COVID-19 pandemic, chest CT, lung segmentation, domain shift, cross-country data, data privacy, annotation burden reduction
- MeSH terms
- Supervised Machine Learning, COVID-19, China, Humans, Italy, Japan, Tomography, X-Ray Computed
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