Imaging of the human body using a number of different modalities has revolutionized the field of medicine over the past several decades and continues to grow at a rapid pace [2] . More than ever, previously unknown information about biology and disease is being unveiled at a range of spatiotemporal scales. Although results and clinical adoption of strategies related to the computational and quantitative analysis of the images have lagged behind development of image acquisition approaches, there has been a noticeable increase of effort and interest in these areas in recent years [6] . This special issue aims to define and highlight some of the “hot” newer ideas that are in biomedical imaging and analysis, intending to shine a light on where the field might move in the next several decades, and focuses on emphasizing where electrical engineers have been involved and could potentially have the most impact. These areas include image acquisition physics, image/signal processing, and image analysis, including pattern recognition and machine learning. This issue focuses on two themes common in much of this effort: first, engineers and computer scientists have found that the information contained in medical images, when viewed through image-based vector spaces, is generally quite sparse. This observation has been transformative in many ways and is quite pervasive in the articles we include here. Second, medical imaging is one of the largest producers of “big data,” and, data-driven machinelearning techniques (e.g., deep learning) are gaining significant attention because improved performance over previous approaches. Thus, data-driven techniques, e.g., formation via image reconstruction [11] and image analysis via deep learning [8] , [9] , are gaining momentum in their development.

中文翻译：

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大数据和深度学习时代的生物医学成像与分析

在过去的几十年里，使用多种不同方式的人体成像已经彻底改变了医学领域，并继续快速发展 [2]。比以往任何时候都多，以前未知的生物学和疾病信息正在一系列时空尺度上被揭开。尽管与图像计算和定量分析相关的策略的结果和临床采用落后于图像采集方法的发展，但近年来在这些领域的努力和兴趣显着增加 [6]。本期特刊旨在定义和突出生物医学成像和分析领域的一些“热门”新想法，旨在阐明该领域在未来几十年可能发展的方向，并着重强调电气工程师参与的领域以及可能产生最大影响的领域。这些领域包括图像采集物理、图像/信号处理和图像分析，包括模式识别和机器学习。本期重点关注大部分工作中常见的两个主题：首先，工程师和计算机科学家发现医学图像中包含的信息，当通过基于图像的向量空间查看时，通常非常稀疏。这种观察在很多方面都具有变革性，并且在我们这里包含的文章中非常普遍。其次，医学影像是“大数据”的最大生产者之一，并且数据驱动的机器学习技术（例如深度学习）由于其性能优于以前的方法而受到广泛关注。因此，