克服医疗保健深度学习中的计算资源限制:针对数据、模型和计算的策略

Han Yuan
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引用次数: 0

摘要

深度学习已经被认为是健康数据科学中不可或缺的支柱。但是,必须考虑许多医疗保健提供商面临的计算限制,他们可能无法访问高性能计算资源。这篇评论从数据、模型和计算的角度阐述了在资源有限的环境中减轻计算约束的三种代表性策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Overcoming Computational Resource Limitations in Deep Learning for Healthcare: Strategies Targeting Data, Model, and Computing

Overcoming Computational Resource Limitations in Deep Learning for Healthcare: Strategies Targeting Data, Model, and Computing

Deep learning has been identified as an indispensable backbone in health data science. However, the computational constraints faced by many healthcare providers, who may lack access to high-performance computing resources, must be considered. This commentary illustrates three representative strategies from the perspective of data, model, and computing to mitigate computational constraints in resource-limited settings.

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