Edge deep learning in computer vision and medical diagnostics: a comprehensive survey

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Review Pub Date : 2025-01-17 DOI:10.1007/s10462-024-11033-5
Yiwen Xu, Tariq M. Khan, Yang Song, Erik Meijering
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Abstract

Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental factors through the close integration of computational resources and data sources. Here we provide a comprehensive review of the current state of the art in edge deep learning, focusing on computer vision applications, in particular medical diagnostics. An overview of the foundational principles and technical advantages of edge deep learning is presented, emphasising the capacity of this technology to revolutionise a wide range of domains. Furthermore, we present a novel categorisation of edge hardware platforms based on performance and usage scenarios, facilitating platform selection and operational effectiveness. Following this, we dive into approaches to effectively implement deep neural networks on edge devices, encompassing methods such as lightweight design and model compression. Reviewing practical applications in the fields of computer vision in general and medical diagnostics in particular, we demonstrate the profound impact edge-deployed deep learning models can have in real-life situations. Finally, we provide an analysis of potential future directions and obstacles to the adoption of edge deep learning, with the intention to stimulate further investigations and advancements of intelligent edge deep learning solutions. This survey provides researchers and practitioners with a comprehensive reference shedding light on the critical role deep learning plays in the advancement of edge computing applications.

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计算机视觉和医学诊断中的边缘深度学习:综合调查
边缘深度学习是一种协调边缘计算和深度学习的范式变化,通过计算资源和数据源的紧密集成,促进了与环境因素相适应的实时决策。在这里,我们对边缘深度学习的现状进行了全面的回顾,重点是计算机视觉应用,特别是医疗诊断。概述了边缘深度学习的基本原理和技术优势,强调了该技术在广泛领域的革命性能力。此外,我们提出了一种基于性能和使用场景的边缘硬件平台的新分类,促进了平台的选择和运营效率。在此之后,我们深入研究了在边缘设备上有效实现深度神经网络的方法,包括轻量级设计和模型压缩等方法。回顾计算机视觉领域的实际应用,特别是医学诊断领域,我们展示了边缘部署深度学习模型在现实生活中可能产生的深远影响。最后,我们对采用边缘深度学习的潜在未来方向和障碍进行了分析,旨在促进智能边缘深度学习解决方案的进一步研究和进步。这项调查为研究人员和实践者提供了全面的参考,揭示了深度学习在边缘计算应用的进步中所起的关键作用。
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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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