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A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
Proceedings of the IEEE ( IF 10.252 ) Pub Date : 2021-02-26 , DOI: 10.1109/jproc.2021.3054390
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram Van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.

中文翻译:

医学影像深度学习的回顾:影像特征,技术趋势,具有重点进展的案例研究以及未来的前景

自其复兴以来,深度学习已广泛用于各种医学成像任务,并在许多医学成像应用中取得了显著成功,从而将我们带入了所谓的人工智能(AI)时代。众所周知,人工智能的成功主要归功于具有单个任务注释的大数据的可用性以及高性能计算的进步。但是,医学成像提出了深度学习方法面临的独特挑战。在本调查报告中,我们首先介绍医学成像的特征,强调医学成像的临床需求和技术挑战,并描述深度学习的新兴趋势如何解决这些问题。我们涵盖了网络架构,稀疏和嘈杂的标签,联合学习,可解释性,不确定性量化,然后,我们介绍一些在临床实践中常见的案例研究,包括数字病理学以及胸部,脑,心血管和腹部成像。我们没有介绍详尽的文献调查,而是描述了与这些案例研究应用相关的一些突出的研究重点。最后,我们讨论并提出了有希望的未来方向。
更新日期:2021-05-04
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