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Review
. 2019 Apr;49(4):939-954.
doi: 10.1002/jmri.26534. Epub 2018 Dec 21.

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

Maciej A Mazurowski  1   2   3 , Mateusz Buda  1 , Ashirbani Saha  1 , Mustafa R Bashir  1   4
Affiliations
Review

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

Maciej A Mazurowski et al. J Magn Reson Imaging. 2019 Apr.

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.

Keywords: artificial intelligence; convolutional neural networks; deep learning; machine learning; medical imaging; radiology.

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Figures

Fig. 1
Fig. 1
A diagram illustrating basic concepts of artificial neural network: (A) a model of a neuron where x1, …, xn are the network inputs, w1,…,wn are the weights, b is a bias, f is the activation function, and y is the neuron output, (B) two common activation functions, (C) a model of a simple neural network.
Fig. 2
Fig. 2
A diagram illustrating basic concepts of convolutional neural networks: (A) a convolutional layers: values in the convolutional filters implemented in the network weights (middle column) are multiplied by the pixel values and the products are summed up, (B) a max pooling layer: a maximum pixel value is taken in a given region, (C) an architecture of a simple convolutional neural network including convolutional, pooling, and fully connected layers.
Fig. 3
Fig. 3
An illustration of different ways of training in deep neural networks: training from scratch, transfer learning, and deep features
Fig. 4
Fig. 4
An illustration of difference between “traditional” machine learning and deep learning. In the “traditional” machine learning, a set of predefined features is extracted and used by a multivariate classifier. In deep learning the entire image is provided as an input to a neural network which outputs a decision.
Fig. 5
Fig. 5
Examples of applications of deep neural network to medical images in our laboratory: (A) A classification task in which a CNN was designed to distinguish between different genomic subtypes (cluster of clusters) of lower grade gliomas in MRI, (B) An automatic segmentation of low grade glioma tumors in MRI, (C) A detection of thyroid nodules in ultrasound
See this image and copyright information in PMC

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