Biomedical Imaging and Analysis in the Age of Big Data and Deep Learning [Scanning the Issue] - Duncan, James S.; Insana, Michael F.; Ayache, Nicholas (Proceedings of the IEEE, 2020)
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.