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Journal Articles European Radiology Year : 2019

Deep learning: definition and perspectives for thoracic imaging


Relevance and penetration of machine learning in clinical practice is a recent phenomenon with multiple applications being currently under development. Deep learning—and especially convolutional neural networks (CNNs)—is a subset of machine learning, which has recently entered the field of thoracic imaging. The structure of neural networks, organized in multiple layers, allows them to address complex tasks. For several clinical situations, CNNs have demonstrated superior performance as compared with classical machine learning algorithms and in some cases achieved comparable or better performance than clinical experts. Chest radiography, a high-volume procedure, is a natural application domain because of the large amount of stored images and reports facilitating the training of deep learning algorithms. Several algorithms for automated reporting have been developed. The training of deep learning algorithm CT images is more complex due to the dimension, variability, and complexity of the 3D signal. The role of these methods is likely to increase in clinical practice as a complement of the radiologist’s expertise. The objective of this review is to provide definitions for understanding the methods and their potential applications for thoracic imaging.
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hal-02422513 , version 1 (22-12-2019)



Maria Vakalopoulou, Guillaume Chassagnon, Nikos Paragios, Marie-Pierre Revel. Deep learning: definition and perspectives for thoracic imaging. European Radiology, 2019, 30, p. 2021-2030. ⟨10.1007/s00330-019-06564-3⟩. ⟨hal-02422513⟩
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