Cited by 137Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

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Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data

Yan, Zengqiang, Wicaksana, Jeffry, Wang, Zhiwei, Yang, Xin, Cheng, Kwang-Ting·2021·IEEE Journal of Biomedical and Health Informatics

DOI 10.1109/jbhi.2020.3040015

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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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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)
ORCID: Yan, Zengqiang
Wicaksana, Jeffry
Wang, Zhiwei
Yang, Xin
Cheng, Kwang-Ting
Venue: IEEE Journal of Biomedical and Health Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-07
Volume: 25
Issue: 7
Pages: 2615-2628
ISSN: 2168-2194 (ISSN-L)
2168-2208
DOI: 10.1109/jbhi.2020.3040015
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data

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