Abstract
It has been well accepted that artificial intelligence, machine learning, and deep learning (AI/ML/DL) techniques are on the path to making a revolutionary impact on health care. However, applying AI/MD/DL for clinical use is still challenging for reasons such as the concerns of security and privacy for sharing patient data among medical institutions, and medical data being heterogeneous. To solve those challenges, Federated learning (FL), which enables distributed training of DL networks without sharing data, could be a feasible solution as shown by the successful demonstration of FL in computer vision tasks. In the medical domain, Federated Tumor Segmentation(FeTS) challenge is the first challenge to address image segmentation using federated learning. In this paper, we evaluated and compared two FL frameworks, FedML and NVIDIA FLARE (NVFLARE), for FeTS 2022 challenge dataset in both distributed and centralized training methods. Using UNet as a baseline network, both FedML and NVFLARE are able to train the UNet models, and the accuracies of the two models are close to the accuracy of the model trained from centralized training. Models trained by NVFLARE performs a little better than models trained by FedML.
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Acknowledgment
We acknowledge support from the National Science Foundation under Grant 2015254, from the University of Southern California, and from the University of Texas at Anderson Cancer Center, Texas Advanced Computing Center, and Oden Institute for Computational and Engineering Sciences initiative in Oncological Data and Computational Science.
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Shi, Y., Gao, H., Avestimehr, S., Yan, Y. (2023). Experimenting FedML and NVFLARE for Federated Tumor Segmentation Challenge. In: Bakas, S., et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 14092. Springer, Cham. https://doi.org/10.1007/978-3-031-44153-0_22
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