Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising

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Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising
Title:
Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising
Journal Title:
IEEE Journal of Biomedical and Health Informatics
Publication Date:
08 May 2023
Citation:
Qiu, L., Cheng, J., Gao, H., Xiong, W., & Ren, H. (2023). Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising. IEEE Journal of Biomedical and Health Informatics, 27(10), 4672–4683. https://doi.org/10.1109/jbhi.2023.3274498
Abstract:
Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learning predictive model from diverse and complex e-health datasets. Federated Learning (FL), as a collaborative machine learning technique, aims to address the challenges by learning a joint predictive model across multi-site clients, especially for distributed medical institutions or hospitals. However, most existing FL methods assume that clients possess fully labeled data for training, which is often not the case in e-health datasets due to high labeling costs or expertise requirement. Therefore, this work proposes a novel and feasible approach to learn a Federated Semi-Supervised Learning (FSSL) model from distributed medical image domains, where a federated pseudo-labeling strategy for unlabeled clients is developed based on the embedded knowledge learned from labeled clients. This greatly mitigates the annotation deficiency at unlabeled clients and leads to a cost-effective and efficient medical image analysis tool. We demonstrated the effectiveness of our method by achieving significant improvements compared to the state-of-the-art in both fundus image and prostate MRI segmentation tasks, resulting in the highest Dice scores of 89.23 and 91.95 respectively even with only a few labeled clients participating in model training. This reveals the superiority of our method for practical deployment, ultimately facilitating the wider use of FL in healthcare and leading to better patient outcomes.
License type:
Publisher Copyright
Funding Info:
The work was supported by Hong Kong Research Grants Council (RGC) Collaborative Research Fund (CRF C4026-21GF and CRF C4063-18G), and General Research Fund (GRF \#14211420 and GRF \#14216022); Shun Hing Institute of Advanced Engineering (BME-p1-21/8115064) at the CUHK; and Shenzhen-Hong Kong-Macau Technology Research Programme (Type C) Grant 202108233000303.
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2168-2194
2168-2208
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