The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.
Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data
Abstract
Privacy concerns make it infeasible to construct a large medical image dataset by fusing small ones from different sources/institutions. Therefore, federated learning (FL) becomes a promising technique to learn from multi-source decentralized data with privacy preservation. However, the cross-client variation problem in medical image data would be the bottleneck in practice. In this paper, we propose a variation-aware federated learning (VAFL) framework, where the variations among clients are minimized by transforming the images of all clients onto a common image space. We first select one client with the lowest data complexity to define the target image space and synthesize a collection of images through a privacy-preserving generative adversarial network, called PPWGAN-GP. Then, a subset of those synthesized images, which effectively capture the characteristics of the raw images and are sufficiently distinct from any raw image, is automatically selected for sharing with other clients. For each client, a modified CycleGAN is applied to translate its raw images to the target image space defined by the shared synthesized images. In this way, the cross-client variation problem is addressed with privacy preservation. We apply the framework for automated classification of clinically significant prostate cancer and evaluate it using multi-source decentralized apparent diffusion coefficient (ADC) image data. Experimental results demonstrate that the proposed VAFL framework stably outperforms the current horizontal FL framework. As VAFL is independent of deep learning architectures for classification, we believe that the proposed framework is widely applicable to other medical image classification tasks.
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“Moreover, we also realize that FL plays a significant role in supporting medical imaging applications, by fusing different medical institutions at a centralized entity via the federated data training process [98].”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [98]
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Metadata
- Authors
- Yan, Z., Wicaksana, J., Wang, Z., Yang, X., & Cheng, K.-T.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Huazhong University of Science and Technology (CN)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2021-07
- Volume
- 25
- Issue
- 7
- Pages
- 2615-2628
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W3107887114
- Cited by
- 137
- References
- 32
- Citations by year
- 2026: 12 · 2025: 32 · 2024: 28 · 2023: 30 · 2022: 29 · 2021: 6
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Hong Kong General Research Fund (16203319)
- WeBank and NSFC (61872417)
- 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, medical imaging data, privacy preservation, generative adversarial networks, CycleGAN, prostate cancer classification, apparent diffusion coefficient, data sharing
- MeSH terms
- Humans, Male, Prostatic Neoplasms, Privacy
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