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Cited by 139Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease

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Linardos, Akis, Kushibar, Kaisar, Walsh, Sean, Gkontra, Polyxeni, Lekadir, Karim·2022·Scientific Reports
DOI 10.1038/s41598-022-07186-4PMC PMC8894335
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Abstract

Deep learning models can enable accurate and efficient disease diagnosis, but have thus far been hampered by the data scarcity present in the medical world. Automated diagnosis studies have been constrained by underpowered single-center datasets, and although some results have shown promise, their generalizability to other institutions remains questionable as the data heterogeneity between institutions is not taken into account. By allowing models to be trained in a distributed manner that preserves patients’ privacy, federated learning promises to alleviate these issues, by enabling diligent multi-center studies. We present the first simulated federated learning study on the modality of cardiovascular magnetic resonance and use four centers derived from subsets of the M&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy. We adapt a 3D-CNN network pretrained on action recognition and explore two different ways of incorporating shape prior information to the model, and four different data augmentation set-ups, systematically analyzing their impact on the different collaborative learning choices. We show that despite the small size of data (180 subjects derived from four centers), the privacy preserving federated learning achieves promising results that are competitive with traditional centralized learning. We further find that federatively trained models exhibit increased robustness and are more sensitive to domain shift effects.

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  • Linardos et al. [26] considered FL for diagnosing hypertrophic cardiomyopathy, whether subjects are suffering from HCM or normal.

    “Linardos et al. [26] considered FL for Diagnosing Hypertrophic Cardiomyopathy (HCM), whether the subjects are suffering from HMC or normal.”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [26]

  • Li et al. [20] and Linardos et al. [26] both used MRI images for their models, brain and heart MRI data respectively.

    “In addition, Li et al. [20] and Linardos et al. [26] both used MRI images for their models, brain and heart MRI data consequently.”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [26]

  • 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 [26]

  • ResNet18 was used in [23], [26], [27], and [29]; ResNet50 in [18] and [24]; ResNet101 in [24].

    “Residual Network (ResNet) is also a commonly used algorithm that can be constructed by different numbers of layers, e.g., ResNet18 ( [23], [26], [27], [29]), ResNet50 ( [18], [24]), ResNet101 ( [24]).”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [26]

  • In [26], Linardos et al. considered AUC, and the highest score achieved by the FL model was 89%.

    “In [26], Linardos et al. considered AUC, the highest score achieved by the FL model was 89%.”

    From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [26]

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Research works printed but not linked: 3

  1. [4]McMahan, B., Moore, E., Ramage, D., Hampson, S. & y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, 1273–1282 (PMLR, 2017).
  2. [5]Bakas, S. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018). · arXiv
  3. [41]Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (2010).

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Authors
Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K.
Affiliations
Universitat de Barcelona (ES)
Corresponding author
Linardos, Akis
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2022-03-03
Volume
12
Issue
1
Pages
3551-3551
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-022-07186-4
PMC
PMC8894335
OpenAlex
W4214841549
Cited by
130
References
53
Citations by year
2026: 40 · 2025: 39 · 2024: 25 · 2023: 19 · 2022: 7
FWCI
7.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
European Union’s Horizon 2020 research and innovation programme (952103)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
federated learning, cardiovascular magnetic resonance, hypertrophic cardiomyopathy, data heterogeneity, privacy-preserving machine learning, 3D convolutional neural networks, data augmentation, domain shift, model generalization, ACDC dataset
MeSH terms
Cardiovascular Diseases, Computer Simulation, Confidentiality, Humans, Magnetic Resonance Imaging, Privacy

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