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...
| Publicado en: | European Radiology Vol. 30; no. 4; pp. 2021 - 2031 |
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| Autores principales: | , , , |
| Formato: | review Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142141887&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142141887 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2020 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142141887 142141887 NLM31811431 142141887 10.1007/s00330-019-06564-3 NLM31811431 142141887 ppf: 2021 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning: definition and perspectives for thoracic imaging. aug: au: Chassagnon, Guillaume Vakalopolou, Maria Paragios, Nikos Revel, Marie-Pierre affil: Service de Radiologie A, Radiology Department, Groupe Hospitalier Cochin Broca Hôtel-Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France sug: subj: Specialties, Medical Methods Algorithms Radiography, Thoracic Scales ab: 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. KEY POINTS: • Deep learning outperforms other machine learning techniques for number of tasks in radiology. • Convolutional neural network is the most popular deep learning architecture in medical imaging. • Numerous deep learning algorithms are being currently developed; some of them may become part of clinical routine in the near future. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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