Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-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 Neuroimaging Clinics
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, et al. Advances in neural information processing systems. Lake Tahoe (NV): Curran Associates, Inc; 2012. p. 1097–105.
- Natural language processing and its future in medicine.Acad Med. 1999; 74: 890-895
- Natural language processing in radiology: a systematic review.Radiology. 2016; 279: 329-343
- Natural Language Processing Technologies in Radiology Research and Clinical Applications.Radiographics. 2016; 36: 176-191
- Deep learning: a primer for radiologists.Radiographics. 2017; 37: 2113-2131
Webster JJ, Kit C. Tokenization as the initial phase in NLP. In: COLING 1992 Volume 4: The 15th International Conference on Computational Linguistics. Nantes (France), August 23-28, 1992.
Silva C, Ribeiro B. The importance of stop word removal on recall values in text categorization. In: Proceedings of the International Joint Conference on Neural Networks. Portland (OR), July 20, 2003.
Balakrishnan V, Lloyd-Yemoh E. Stemming and lemmatization: a comparison of retrieval performances. In: Proceedings of SCEI Seoul Conferences. Seoul (Korea), April 10-11, 2014.
Plisson J, Lavrac N, Mladenic D. A rule based approach to word lemmatization. Proceedings of IS-2004. Salt Lake City (UT), May 3-5, 2004.
Brill E. A simple rule-based part of speech tagger. In Proceedings of the third conference on Applied natural language processing. Association for Computational Linguistics. Newark (DE), June 28-July 2, 1992.
Navigli R. Word sense disambiguation: A survey. ACM computing surveys (CSUR) 2009;41(2):1-69.
- Understanding bag-of-words model: a statistical framework.International Journal of Machine Learning and Cybernetics. 2010; 1: 43-52https://doi.org/10.1007/s13042-010-0001-0
- Natural language processing and information retrieval.in: Pazienza M.T. International summer School on information extraction. Springer, Berlin1999: 32-48
Ramos J. Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning. Piscataway (NJ), December 3-8, 2003.
Taylor SJ, Harabagiu SM. The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports. In AMIA Annual Symposium Proceedings. American Medical Informatics Association. San Francisco (CA), November 3-7, 2018.
- A simple algorithm for identifying negated findings and diseases in discharge summaries.J Biomed Inform. 2001; 34: 301-310
- DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx.J Biomed Inform. 2015; 54: 213-219
Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems 2013. pp. 3111–9.
Pennington J, Socher R, Manning C. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha (Qatar), October 25-29, 2014.
Banerjee I, Madhavan S, Goldman RE, et al. Intelligent word embeddings of free-text radiology reports. In AMIA Annual Symposium Proceedings. American Medical Informatics Association. Brussels (Belgium), October 31-November 4, 2018.
- Representation of events in nerve nets and finite automata.RAND PROJECT AIR FORCE SANTA MONICA CA, 1951
- Programming techniques: Regular expression search algorithm.Commun ACM. 1968; 11: 419-422
- Automatic generation of efficient lexical processors using finite state techniques.Commun ACM. 1968; 11: 805-813
- Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification.Baltic J Mod Comput. 2017; 5: 221-232
- Deep learning.Nature. 2015; 521: 436-444
- Object recognition with gradient-based learning.in: Forsyth D.A. Mundy J.L. di Gesu V. Shape, contour and grouping in computer vision. Springer, Berlin1999: 319-345
Kim Y. Convolutional neural networks for sentence classification. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha (Qatar), October 25-29, 2014.
- Finding structure in time.Cogn Sci. 1990; 14: 179-211
- Long short-term memory.Neural Comput. 1997; 9: 1735-1780
Lei T, Zhang Y, Wang SI, et al. Simple recurrent units for highly parallelizable recurrence. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing 2018, 4470-4481.
Howard J, Ruder S. Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne (Australia), July 15-20, 2018.
- BERT: pre-training of deep bidirectional transformers for language understanding.North American Association for Computational Linguistics (NAACL), Minneapolis (MN)2019
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Guyon I, Luxburg UV, Bengio S, et al, editors. Advances in Neural Information Processing Systems 30. Curran Associates, Inc; 2017. p. 5998-6008.
You Y, Li J, Hseu J, et al. 2019. Reducing bert pre-training time from 3 days to 76 minutes. In Proceedings of the Eight International Conference on Learning Representations. Addis Ababa (Ethiopia), April 26-May 1, 2020.
- Deep contextualized word representations.North American Association for Computational Linguistics (NAACL), New Orleans (LA)2018
Liu Y, Ott M, Goyal N, et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach. In Proceedings of the Eight International Conference on Learning Representations. Addis Ababa (Ethiopia), April 26-May 1, 2020.
Conneau A, Lample G. Cross-lingual Language Model Pretraining. In Advances in Neural Information Processing Systems. Vancouver (Canada), December 8-14, 2019.
- Language models are unsupervised multitask learners. Technical report.OpenAI, San Francisco (CA)2019
- Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.J Am Med Inform Assoc. 2018; 25: 568-571
- Deep Learning for Comprehensive Automated Radiology Protocolling. In: SIIM 2018 Proceedings.(Available at:)Accessed November 1, 2019)
- Integrating natural language processing and machine learning algorithms to categorize oncologic response in radiology reports.J Digit Imaging. 2018; 31: 178-184
- Natural language processing of radiology reports for identification of skeletal site-specific fractures.BMC Med Inform Decis Mak. 2019; 19: 73
- Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports.Radiology. 2002; 224: 157-163
- Automatic classification of mammography reports by BI-RADS breast tissue composition class.J Am Med Inform Assoc. 2012; 19: 913-916
- Natural language processing using online analytic processing for assessing recommendations in radiology reports.J Am Coll Radiol. 2008; 5: 197-204
- Initial effectiveness of a monitoring system to correctly identify inappropriate lack of follow-up for abdominal imaging findings of possible cancer.J Am Coll Radiol. 2016; 13: 1505-1508
- Deep learning to classify radiology free-text reports.Radiology. 2017; 286: 845-852
- Association of lower diagnostic yield with high users of CT pulmonary angiogram.JAMA Intern Med. 2018; 178: 412-413
- Automated radiology report summarization using an open-source natural language processing pipeline.J Digit Imaging. 2018; 31: 185-192
- Automated detection of measurements and their descriptors in radiology reports using a hybrid natural language processing algorithm.J Digit Imaging. 2019; 32: 544-553
- Novel use of chatbot technology to educate patients before breast biopsy.J Am Coll Radiol. 2019; 16: 1305-1308