Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
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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 federated learning study on the modality of cardiovascular magnetic resonance (CMR) and use four centers derived from subsets of the M&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM). 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 setups , 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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FL has developed a system that makes it possible for doctors to use data from multiple medical institutions without sharing patient data across them.
“FL has developed a system that makes it possible for doctors to use data from multiple medical institutions without sharing patient data across them [62].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [62]
Given the fact that there can be no comprehensive information distribution among institutions, FL can be trained using non-IID data.
“Given the fact that there can be no comprehensive information distribution among institutions [56, 62], FL can be trained using non-IID data.”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [62]
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- Authors
- Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K.
- Affiliations
- Universitat de Barcelona (ES)
- Radiomics
- Corresponding author
- Linardos, Akis
- Venue
- Research Square
- Publisher
- Research Square Platform LLC
- Published
- 2021-07-09
- OpenAlex
- W3179176383
- Cited by
- 9
- References
- 63
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 6
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- Research Square (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (HORIZON2020, 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
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