A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.
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DOI：
10.1109/jproc.2021.3054390
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发表时间：
2021-05
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自其复兴以来，深度学习被广泛应用于各种医学成像任务中，并在许多医学成像应用中取得了显著的成功，从而将我们推向了所谓的人工智能(AI)时代。众所周知，人工智能的成功很大程度上归功于对单个任务进行注释的大数据的可用性，以及高性能计算的进步。然而，医学成像是深度学习方法面临的独特挑战。在这篇调查论文中，我们首先介绍了医学成像的特点，强调了医学成像的临床需求和技术挑战，并描述了深度学习的新兴趋势如何解决这些问题。我们涵盖了网络结构、稀疏和噪声标签、联合学习、可解释性、不确定性量化等主题。然后，我们提供了几个在临床实践中常见的案例研究，包括数字病理学和胸部、脑、心血管和腹部成像。而不是提供详尽的文献调查，相反，我们描述了一些与这些案例研究应用相关的突出研究亮点。最后，我们对未来的发展方向进行了讨论和介绍。
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.