Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.
Radiology. 2019 Mar;290(3):590-606. doi: 10.1148/radiol.2018180547. Epub 2019 Jan 29.
Soffer S1, Ben-Cohen A1, Shimon O1, Amitai MM1, Greenspan H1, Klang E1.
Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an introduction to deep learning technology and presents the stages that are entailed in the design process of deep learning radiology research. In addition, the article details the results of a survey of the application of deep learning-specifically, the application of convolutional neural networks-to radiologic imaging that was focused on the following five major system organs: chest, breast, brain, musculoskeletal system, and abdomen and pelvis. The survey of the studies is followed by a discussion about current challenges and future trends and their potential implications for radiology. This article may be used as a guide for radiologists planning research in the field of radiologic image analysis using convolutional neural networks.