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

2018/12/21 by Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha +1 · 6 citations
Computer Science · Medicine · #AI in cancer detection #COVID-19 diagnosis using AI #Radiomics and Machine Learning in Medical Imaging

paper · doi:10.1002/jmri.26534

openalex publication_date 2018/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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 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. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.

Citations

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• Multimodal MR Synthesis via Modality-Invariant Latent Representation
• A systematic study of the class imbalance problem in convolutional neural networks
• A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

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