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：

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医学影像深度学习综述：影像特征、技术趋势、进展亮点案例研究以及未来前景


自复兴以来，深度学习已广泛应用于各种医学影像任务，并在许多医学影像应用中取得了令人瞩目的成功，从而推动我们进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带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。然而，医学成像给深度学习方法带来了独特的挑战。在这篇调查论文中，我们首先介绍了医学成像的特征，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。我们涵盖了网络架构、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，我们介绍了临床实践中常见的几个案例研究，包括数字病理学和胸部、大脑、心血管和腹部成像。我们没有提供详尽的文献调查，而是描述了与这些案例研究应用相关的一些突出的研究亮点。最后，我们讨论并介绍了有希望的未来方向。