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

2018/02/10 by Maciej A. Mazurowski, Mazurowski, Maciej A., Mateusz Buda +5 · 5 citations
Computer Science · Mathematics · Medicine · #AI in cancer detection #Applications (stat.AP) #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Radiomics and Machine Learning in Medical Imaging #cs.CV #cs.LG #stat.AP #stat.ML

paper · pdf · doi:10.48550/arxiv.1802.08717

27 pages, 4 figures

arxiv created 2018/02/10 · openalex publication_date 2018/02/10 · arxiv updated 2018/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Scalable, High-Quality Object Detection
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Long Short-Term Memory
• An Analysis of Deep Neural Network Models for Practical Applications
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• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
• Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
• Multimodal MR Synthesis via Modality-Invariant Latent Representation
• DeepRadiologyNet: Radiologist Level Pathology Detection in CT Head Images
• Detecting hip fractures with radiologist-level performance using deep neural networks
• Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
• Deep Learning for identifying radiogenomic associations in breast cancer
• Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians
• H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

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