Nakata Norio
Department of Radiology, The Jikei University, School of Medicine, 3-25-8, Nishi-shimbashi, Minato-ku, Tokyo, 1058461, Japan.
Jpn J Radiol. 2019 Feb;37(2):103-108. doi: 10.1007/s11604-018-0804-6. Epub 2019 Jan 31.
Deep 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.
深度学习引发了人工智能的第三次热潮,预计在不久的将来,放射学、病理学、视网膜成像、皮肤科检查和内镜诊断等诊断医学成像系统将发生巨大变化。然而,近年来人们提出了各种深度学习的尝试和新方法,其发展速度极快。因此,在医学人工智能论文发表的初期,人工智能技术本身可能已是旧技术或广为人知的通用常规技术。因此,作者回顾了2018年使用深度学习技术的前沿计算机视觉论文和报告,这些技术从放射科医生的角度来看具有未来临床潜力,如生成对抗网络、知识蒸馏以及用于监督学习的通用图像数据集。