Utilization of deep learning in diagnostic radiology

Abstract

At the ImageNet Large Scale Visual Recognition Competition（ILSVRC）2012, Hinton et al showed a higher recognition rate and won the victory of this competition. Since then, deep learning has become a focus of attention. In the field of image recognition, the convolutional neural network（CNN）, which is one of the deep learning methods, is most frequently used. Prior to the deep learning era, the boom of AI has been twice so far, and Deep Learning caused a third AI boom. Due to deep learning, accuracy of computer image recognition and speech recognition is dramatically improving. Regarding the image, at the ILSVRC mentioned above, in 2015, AI reached beyond human image recognition ability. The innovation of AI this time is merely an improvement of the image recognition ability of the computer. However, if radiologists increase the amount of reading by using AI in the future, the clinical importance of radiologists will be higher than now. Currently, application of AI to image diagnosis area, which each company is developing, is not limited to applying Deep Learning to image recognition, but also by constructing a system combined with various existing AI technologies.