Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.

By S. Soffer, Avi Ben-Cohen, O. Shimon, Michal Marianne Amitai, Hayit Greenspan, Eyal Klang. 2019

469 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Daniel L. Rubin
2. Ronald M. Summers
3. Les R. Folio
4. Matthew P. Lungren

In Radiology, Nuclear Medicine and Imaging

1. Matthew P. Lungren
2. Daniel L. Rubin
3. Dominik Fleischmann
4. Ronald M. Summers
5. Martin J. Willemink

Citations in the corpus

• Preparing Medical Imaging Data for Machine Learning (2020, background): “Currently, most research groups and companies have limited access to medical images, while the small sample sizes and lack of diverse geographic areas hinder the generalizability and accuracy of developed solutions (21).”