Abstract— Recently, a machine learning (ML) area called deep learning emerged in the computer-vision field and became very popular in many fields. When a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, Image Net Classification. Since then, researchers in many fields, including medical image analysis, have started actively participating in the explosively growing field of deep learning. In this paper, deep learning techniques and their applications to medical image analysis are surveyed. This survey overviewed 1) standard ML techniques in the computer-vision field, 2) what has changed in ML before and after the introduction of deep learning, 3) ML models in deep learning, and 4) applications of deep learning to medical image analysis. The survey of deep learning also revealed that there is a long history of deep-learning techniques in the class of ML with image input, except a new term, “deep learning”. ML with image input including deep learning is a very powerful, versatile technology with higher performance, which can bring the current state-of-the-art performance level of medical image analysis to the next level, and it is expected that deep learning will be the mainstream technology in medical image analysis in the next few decades.