Keywords
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribers receive full online access to your subscription and archive of back issues up to and including 2002.
Content published before 2002 is available via pay-per-view purchase only.
Subscribe:
Subscribe to Radiologic ClinicsReferences
- Classification of diffuse lung diseases: why and how.Radiology. 2013; 268: 628-640
- Interobserver variability in the CT assessment of honeycombing in the lungs.Radiology. 2013; 266: 936-944
- CT densitometry in emphysema: a systematic review of its clinical utility.Int J Chron Obstruct Pulmon Dis. 2018; 13: 547-563
- Comparison of shallow and deep learning methods on classifying the regional pattern of diffuse lung disease.J Digit Imaging. 2018; 31: 415-424
- Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome.Phys Med Biol. 2018; 31: 415-424
- Quantitative CT analysis of diffuse lung disease.Radiographics. 2020; 40: 28-43
- Artificial intelligence, n.in: Oxford English dictionary. Oxford University Press Online Dictionary, 2017 (Accessed 27 July 2022)
- Overview of deep learning in medical imaging.Radiological Phys Technol. 2017; 10: 257-273
Oxford-English-Dictionary. deep learning n. In. Oxford English Dictionary 2022. Online Dictionary. Available at: www.oed.com. Accessed January 4,2022.
- Deep learning.Nature. 2015; 521: 436
- Interstitial lung abnormalities: state of the art.Radiology. 2021; 301: 19-34
- Association between interstitial lung abnormalities and all-cause mortality.JAMA. 2016; 315: 672-681
- Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks.Scientific Rep. 2020; 10: 338
- Quantitative computed tomography imaging of interstitial lung diseases.J Thorac Imaging. 2013; 28: 298-307
- Computer-Aided quantitative analysis in interstitial lung diseases - A pictorial review using CALIPER.Lung India. 2021; 38: 161-167
- Differentiation of Idiopathic pulmonary fibrosis from connective tissue disease-related interstitial lung disease using quantitative imaging.J Clin Med. 2021; 10: 2663
- Vessel-related structures predict UIP pathology in those with a non-IPF pattern on CT.Eur Radiol. 2021; 31: 7295-7302
- Predicting outcomes in idiopathic pulmonary fibrosis using automated computed tomographic analysis.Am J Respir Crit Care Med. 2018; 198: 767-776
- Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures.Eur Respir J. 2017; 49: 1601011
- Second-opinion reads in interstitial lung disease imaging: added value of subspecialty interpretation.J Am Coll Radiol. 2020; 17: 786-790
- Diagnostic ability of a dynamic multidisciplinary discussion in interstitial lung diseases: a retrospective observational study of 938 cases.Chest. 2018; 153: 1416-1423
- Computerized analysis of interstitial infiltrates on chest radiographs: a new scheme based on geometric pattern features and Fourier analysis.Acad Radiol. 1995; 2: 455-462
- Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM).Acad Radiol. 2006; 13: 969-978
- Comparison of shallow and deep learning methods on classifying the regional pattern of diffuse lung disease.J Digit Imaging. 2018; 31: 415-424
- Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images.Invest Radiol. 2019; 54: 627-632
- Deep learning of computed tomography virtual wedge resection for prediction of histologic usual interstitial pneumonitis.Ann Am Thorac Soc. 2020; 18: 51-59
- Lung segmentation on HRCT and volumetric CT for diffuse interstitial lung disease using deep convolutional neural networks.J Digit Imaging. 2019; 32: 1019-1026
- Automated lung segmentation on chest computed tomography images with extensive lung parenchymal abnormalities using a deep neural network.Korean J Radiol. 2021; 22: 476-488
- Interstitial lung disease in systemic sclerosis: a simple staging system.Am J Respir Crit Care Med. 2008; 177: 1248-1254
- Extent of disease on high-resolution computed tomography lung is a predictor of decline and mortality in systemic sclerosis-related interstitial lung disease.Rheumatology (Oxford). 2013; 52: 155-160
- Deep learning-based approach for automated assessment of interstitial lung disease in systemic sclerosis on CT images.Radiol Artif Intell. 2020; 2: e190006
- Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.Radiology. 2021; 298: 189-198
- Quantitative and semi-quantitative computed tomography analysis of interstitial lung disease associated with systemic sclerosis: a longitudinal evaluation of pulmonary parenchyma and vessels.PLoS One. 2019; 14: e0213444
- Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases.Eur Radiol. 2018; 31: 415-424
Irvin J, Rajpurkar P, Ko M, et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI Conference on Artificial Intelligence. 2019;33(01):590-597. Honolulu, Hawaii USA — January 27–February 1, 2019.
- Building a reference multimedia database for interstitial lung diseases.Comput Med Imaging Graph. 2012; 36: 227-238
- Lung tissue research Consortium.National Institutes of Health, 2022 (Available at:) (Accessed 1/02/2022)
- Modeling generalization in machine learning: a methodological and computational study.arXiv. 2018; 31: 415-424
- Deep learning in radiology.Acad Radiol. 2018; 31: 415-424
- Deep learning: a primer for radiologists.RadioGraphics. 2017; 37: 2113-2131
- Machine learning for medical imaging.Radiographics. 2017; 37: 505-515
- Review of artificial intelligence training tools and courses for radiologists.Acad Radiol. 2021; 28: 1238-1252
- Deep learning.The MIT Press, Cambridge (MA)2016
Article info
Publication history
Footnotes
Disclosures: None.