• Daiju Ueda, Akitoshi Shimazaki, and Yukio Miki.
• Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan. ueda.daiju@gmail.com.
• Jpn J Radiol. 2019 Jan 1; 37 (1): 15-33.

AbstractDeep 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.

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