Deep learning in radiology: an overview of the concepts and a survey of the state of the art

TL;DR

This paper provides an overview of deep learning concepts and surveys its recent applications in radiology, highlighting its potential to transform medical image analysis and clinical practice.

Contribution

It offers a comprehensive review of deep learning methods applied to radiology, including basic concepts, research organization, and future opportunities and challenges.

Findings

Deep learning has achieved performance comparable to or exceeding human experts in image analysis.

Numerous studies have applied convolutional neural networks to various radiological tasks.

The field is rapidly evolving with significant potential for clinical integration.

Abstract

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mostly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms. We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review the broad range of utilized deep learning algorithms. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.