Abstract
Training predictive models with decentralized medical data can boost the healthcare research and is important for healthcare applications. Although the federated learning (FL) was proposed to build the predictive models, how to improve the security and robustness of a learning system to resist the accidental or malicious modification of data records are still the open questions. In this paper, we describe DeMed, a privacy-preserving decentralized medical image analysis framework empowered by blockchain technology. While blockchain is limited in serial computing, the decentralized data interaction in blockchain is very desired to preserve the data privacy when training models. To adapt blockchain in medical image analysis, our framework consists of the self-supervised learning part running on users’ local devices and the smart contract part running on blockchain. The prior is to obtain the provable linearly separable low-dimensional representations of local medical images and the latter is to obtain the classifier by synthetically absorbing users’ self-supervised learning results. The proposed DeMed is validated on two independent medical image classification tasks on pathological data and chest X-ray. Our work provides an open platform and arena for FL, where everyone can deploy a smart contract to attract contributors for medical image classification in a secure and decentralized manner while preserving the privacy in medical images.
G. Aggarwal and C.-Y. Huang—These authors contributed equally to this work.
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Notes
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An alternative way is to pre-train MAE using ImageNet and finetune on the collected data afterwards, if the number of the collected data is low.
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Acknowledgement
This work is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada (RGPIN-2021-02970, DGECR-2021-00187, DGECR-2022-00430), NVIDIA Hardware Award, and Public Safety Canada (NS-5001-22170).
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Aggarwal, G., Huang, CY., Fan, D., Li, X., Wang, Z. (2022). DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain. In: Albarqouni, S., et al. Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health. DeCaF FAIR 2022 2022. Lecture Notes in Computer Science, vol 13573. Springer, Cham. https://doi.org/10.1007/978-3-031-18523-6_10
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