Abstract
Federated learning, an innovative artificial intelligence training method, offers a secure solution for institutions to collaboratively develop models without sharing raw data. This approach offers immense promise and is particularly advantageous for domains dealing with sensitive information, such as patient data. However, when confronted with a distributed data environment, challenges arise due to data paucity or inherent heterogeneity, potentially impacting the performance of federated learning models. Hence, scrutinizing the efficacy of this method in such intricate settings is indispensable. To address this, we harnessed pathological image datasets of endometrial cancer from four hospitals for training and evaluating the performance of a federated learning model and compared it with a centralized learning model. With optimal processing techniques (data augmentation, color normalization, and adaptive optimizer), federated learning exhibited lower precision but higher recall and Dice similarity coefficient (DSC) than centralized learning. Hence, considering the critical importance of recall in the context of medical image processing, federated learning is demonstrated as a viable and applicable approach in this field, offering advantages in terms of both performance and data security.
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Data are accessible from the corresponding author by request, subject to approval from the institutional review board.
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Acknowledgements
We extend our heartfelt gratitude to the members of the biomedical engineering lab for their invaluable guidance and significant contributions to the development of the federated model.
Funding
This work was supported by the Gachon University research fund of 2022 (GCU-2022–202209640001) and National Research Foundation of Korea (NRF), funded by the Korean government (MSIT) (2021R1A2B5B02001915).
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Jisup Kim and Kwang Gi Kim contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jong-Chan Yeom, Jae-Hoon Kim and Jisup Kim. The first draft of the manuscript was written by Jong-Chan Yeom and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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This study received approval from the Institutional Review Board (IRB) of Gachon University Gil Medical Center (GBIRB2022-236). Informed consent was waived given the retrospective study design, which presented minimal risk to participants.
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Yeom, J., Kim, J., Kim, Y.J. et al. A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01020-1
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DOI: https://doi.org/10.1007/s10278-024-01020-1