• Norio Nakata.
• Department of Radiology, The Jikei University, School of Medicine, 3-25-8, Nishi-shimbashi, Minato-ku, Tokyo, 1058461, Japan. nakata@jikei.ac.jp.
• Jpn J Radiol. 2019 Feb 1; 37 (2): 103-108.

AbstractDeep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology, retinal imaging, dermatology inspection, and endoscopic diagnosis will be expected in the near future. However, various attempts and new methods of deep learning have been proposed in recent years, and their progress is extremely fast. Therefore, at the initial stage when medical artificial intelligence papers were published, the artificial intelligence technology itself may be old technology or well-known general-purpose common technology. Therefore, the author has reviewed state-of-the-art computer vision papers and presentations of 2018 using deep learning technologies, which will have future clinical potentials selected from the point of view of a radiologist such as generative adversarial network, knowledge distillation, and general image data sets for supervised learning.

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