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

Scientific reports, 2022-03, Vol.12 (1), p.3551-3551, Article 3551 [Peer Reviewed Journal]

2022. The Author(s). ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-022-07186-4 ;PMID: 35241683

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  • Title:
    Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease
  • Author: Linardos, Akis ; Kushibar, Kaisar ; Walsh, Sean ; Gkontra, Polyxeni ; Lekadir, Karim
  • Subjects: Artificial intelligence ; Automation ; Cardiomyopathy ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular Diseases - diagnostic imaging ; Collaboration ; Computer Simulation ; Confidentiality ; Coronaviruses ; COVID-19 ; Datasets ; Deep learning ; Diagnosis ; Heart ; Humans ; Magnetic Resonance Imaging ; Medical imaging ; Privacy
  • Is Part Of: Scientific reports, 2022-03, Vol.12 (1), p.3551-3551, Article 3551
  • Description: 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.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-022-07186-4
    PMID: 35241683
  • Source: MEDLINE
    PubMed Central
    Coronavirus Research Database
    ProQuest Central
    DOAJ Directory of Open Access Journals
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