Ueda Daiju, Shimazaki Akitoshi, Miki Yukio
Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
Jpn J Radiol. 2019 Jan;37(1):15-33. doi: 10.1007/s11604-018-0795-3. Epub 2018 Dec 1.
Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep learning in radiology. To gain a more practical understanding of deep learning, deep learning techniques are divided into five categories: classification, object detection, semantic segmentation, image processing, and natural language processing. After a brief overview of technical network evolutions, clinical applications based on deep learning are introduced. The clinical applications are then summarized to reveal the features of deep learning, which are highly dependent on training and test datasets. The core technology in deep learning is developed by image classification tasks. In the medical field, radiologists are specialists in such tasks. Using clinical applications based on deep learning would, therefore, be expected to contribute to substantial improvements in radiology. By gaining a better understanding of the features of deep learning, radiologists could be expected to lead medical development.
深度学习不仅已应用于放射学的临床应用，还应用于医学的所有其他领域。本综述提供了深度学习在放射学方面的技术和临床概述。为了更实际地理解深度学习，深度学习技术分为五类：分类、目标检测、语义分割、图像处理和自然语言处理。在简要概述技术网络的发展之后，介绍了基于深度学习的临床应用。然后对临床应用进行总结，以揭示深度学习的特征，这些特征高度依赖于训练和测试数据集。深度学习的核心技术是通过图像分类任务发展而来的。在医学领域，放射科医生是此类任务的专家。因此，使用基于深度学习的临床应用有望为放射学带来实质性的改善。通过更好地理解深度学习的特征，有望让放射科医生引领医学发展。
