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10 April 2023 Characterizing browser-based medical imaging AI with serverless edge computing: towards addressing clinical data security constraints
Chenxi Dong, Thomas Z. Li, Kaiwen Xu, Zekun Wang, Fabien Maldonado, Kim Sandler, Bennett A. Landman, Yuankai Huo
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Proceedings Volume 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications; 1246907 (2023) https://doi.org/10.1117/12.2653626
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
Abstract
The deployment of deep learning algorithms in clinical practice faces challenges in data privacy and local hardware constraints. This work presents the tools and design choices of a browser-based edge computing framework to address these challenges. We leverage this framework for 3D medical image segmentation from computed tomography and characterize its speed, memory, and limitations across various operating systems and browsers. Our platform deploys deep learning-based segmentation of a 256×256×256 volume with an average runtime of 80 seconds and average memory usage of 1.5 GB on Firefox, Chrome, and Microsoft Edge using consumer-level laptops.
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Chenxi Dong, Thomas Z. Li, Kaiwen Xu, Zekun Wang, Fabien Maldonado, Kim Sandler, Bennett A. Landman, and Yuankai Huo "Characterizing browser-based medical imaging AI with serverless edge computing: towards addressing clinical data security constraints", Proc. SPIE 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, 1246907 (10 April 2023); https://doi.org/10.1117/12.2653626
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KEYWORDS
Artificial intelligence
Medical imaging
3D modeling
Computer security
Computed tomography
Clouds
Data modeling
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