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Deep Learning In Radiology: An Overview Of The Concepts And A Survey Of The State Of The Art With Focus On MRI

M. Mazurowski, Mateusz Buda, Ashirbani Saha, M. Bashir
Published 2019 · Computer Science, Medicine, Mathematics

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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.
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10.3174/ajnr.A6070
Deep Learning–Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study
G. Fan (2019)
10.1109/ACCESS.2020.3016780
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
Michael J. Horry (2020)
Artificial Intelligence in Medical Imaging
A. A. Khalek (2020)
10.1016/j.clinimag.2020.04.025
Understanding artificial intelligence based radiology studies: What is overfitting?
Simukayi Mutasa (2020)
10.1016/j.physa.2019.122652
Resizing and cleaning of histopathological images using generative adversarial networks
Gaffari Çelik (2020)
10.1109/IPTA.2019.8936082
A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs
J. V. Houtte (2019)
10.1038/s41598-020-72270-6
Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network
Jieun Koh (2020)
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