Machine Learning in Medical Imaging Before and After Introduction of Deep Learning

Abstract

Because a huge amount of new data is generated every day, the data that we treat are “big data”, and they are not something that we can handle manually. Machine learning（ML）that can handle such “big data” automatically becomes a rapidly growing, indispensable area of research in the fields of medical imaging and computer vision. Recently, a terminology, deep learning emerged and became very popular in the computer vision field. It started from an event in 2012 when a deep learning approach based on a convolutional neural network（CNN）won an overwhelming victory in the bestknown worldwide computer-vision competition, ImageNet Classification. Since then, researchers in virtually all fields including medical imaging have started actively participating in the explosively growing field of deep learning. In this paper,the field of machine learning in medical imaging before and after the introduction of deep learning is reviewed to make clear 1）what deep learning is exactly, 2）what was changed before and after the introduction of deep learning, and 3）what is the source of the power of deep learning. This review reveals that object/feature-based ML was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is learning image data directly without object segmentation or feature extraction ; thus, it is the source of the power of deep learning. The class of image/pixel-based ML including deep learning has a long history, but gained the popularity recently due to the new terminology, deep learning. The image/pixel-based ML is a versatile technology with substantially high performance. ML including deep learning in medical imaging is an explosively growing, promising field. It is expected that image/pixel-based ML including deep learning will be the mainstream technology in the field of medical imaging in the next few decades.