Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI

By Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir. 2018

523 citations recorded by the source corpus.

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 mainly 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 discuss the general context of radiology and 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 a broad range of deep‐learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [102]27, covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [103, 104, 105, 106, 107, 108, 109, 110, 111, 1”
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network (2021, unknown): “The advent of Deep Learning has had a tremendous impact in the design of medical imaging systems, and newer techniques have been widely adopted by the researchers over the last few years [21].”
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm (2019, background): “There are many examples of successful applications of deep learning in medical imaging [10, 11, 12, 13, 14] and more specifically in brain MRI segmentation [15].”
Domain Adaptation for Medical Image Analysis: A Survey (2021, unknown): “Trained with large-scale labeled data in a full supervision manner, CNN has made breakthroughs in computer vision and medical image analysis [63]–[65].”
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020, background): “Deep learning artiﬁcial neural networks, and the Convolutional Neural Networks (CNNs) have proven to be highly effective in a vast range of medical image classiﬁcation applications [20], [21].”
Deep Learning for Cardiac Image Segmentation: A Review (2019, background): “To our knowledge, there have been several review papers that presented overviews about applications of DL-based methods for general medical image analysis (5–7), as well as some surveys dedicated to applications designed for cardiovascular image analysis (8, 9).”