R25 SECTION 6 - Natural Language Processing in Radiology R25 VOICE
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- Cursos
Papers discussed in this Section 6 Podcast:
H. Salehinejad, J. Barfett, S. Valaee, E. Colak, A. Mnatzakanian, and T. Dowdell. Interpretation of mammogram and chest radiograph reports using deep neural networks-preliminary results. arXiv preprint arXiv:1708.09254, 2017.Hassanpour S, Langlotz CP. Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning. Acad Radiol 2016; 23 (01) 84-89.Pons E., Braun L.M.M., Hunink M.G.M. et al. (2016) Natural language processing in radiology: a systematic review. Radiology, 279, 329–343.Trivedi, H., Mesterhazy, J., Laguna, B. et al. Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm. J Digit Imaging (2017). https://doi.org/10.1007/s10278-017-0021-3Podcast Contents
Why These PapersNLP ReviewDefining NLPNLP Pipeline in Figure 1RadlexEvaluation Measures - F1TypesDiagnostic SurveillanceCohort BuildingQuery based case retrievalQuality Assessment in radiologic practiceCommunication of critical resultsClinical Support ServicesResources in Table 2Operational BarriersFuture Research NeedsIV ContrastWhy Chosen?NotesProcessing Time DiscussionError analysisCloud ServicePassive Workflow integration.Predicting High Imaging UtilizationWhy Chosen?NotesSVM usage.Document-Feature MatrixOverfitInterpretation of MammogramsWhy Chosen?NotesBi-directional CNNPassive Workflow IntegrationPreprocessingWhy Deep LearningQuestions
Papers discussed in this Section 6 Podcast:
H. Salehinejad, J. Barfett, S. Valaee, E. Colak, A. Mnatzakanian, and T. Dowdell. Interpretation of mammogram and chest radiograph reports using deep neural networks-preliminary results. arXiv preprint arXiv:1708.09254, 2017.Hassanpour S, Langlotz CP. Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning. Acad Radiol 2016; 23 (01) 84-89.Pons E., Braun L.M.M., Hunink M.G.M. et al. (2016) Natural language processing in radiology: a systematic review. Radiology, 279, 329–343.Trivedi, H., Mesterhazy, J., Laguna, B. et al. Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm. J Digit Imaging (2017). https://doi.org/10.1007/s10278-017-0021-3Podcast Contents
Why These PapersNLP ReviewDefining NLPNLP Pipeline in Figure 1RadlexEvaluation Measures - F1TypesDiagnostic SurveillanceCohort BuildingQuery based case retrievalQuality Assessment in radiologic practiceCommunication of critical resultsClinical Support ServicesResources in Table 2Operational BarriersFuture Research NeedsIV ContrastWhy Chosen?NotesProcessing Time DiscussionError analysisCloud ServicePassive Workflow integration.Predicting High Imaging UtilizationWhy Chosen?NotesSVM usage.Document-Feature MatrixOverfitInterpretation of MammogramsWhy Chosen?NotesBi-directional CNNPassive Workflow IntegrationPreprocessingWhy Deep LearningQuestions