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Connection

Jayashree Kalpathy-Cramer to Natural Language Processing

This is a "connection" page, showing publications Jayashree Kalpathy-Cramer has written about Natural Language Processing.

 
Connection Strength
 
 
 
1.914
 
  1. Yang E, Li MD, Raghavan S, Deng F, Lang M, Succi MD, Huang AJ, Kalpathy-Cramer J. Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification? Br J Radiol. 2023 Sep; 96(1149):20220769.
    View in: PubMed
    Score: 0.739
  2. Li MD, Lang M, Deng F, Chang K, Buch K, Rincon S, Mehan WA, Leslie-Mazwi TM, Kalpathy-Cramer J. Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports. AJNR Am J Neuroradiol. 2021 03; 42(3):429-434.
    View in: PubMed
    Score: 0.624
  3. Kalpathy-Cramer J, de Herrera AG, Demner-Fushman D, Antani S, Bedrick S, Müller H. Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013. Comput Med Imaging Graph. 2015 Jan; 39:55-61.
    View in: PubMed
    Score: 0.392
  4. Bedrick S, Kalpathy-Cramer J. A Ferret-based gastrointestinal image retrieval system. AMIA Annu Symp Proc. 2007 Oct 11; 868.
    View in: PubMed
    Score: 0.063
  5. Li MD, Wood PA, Alkasab TK, Lev MH, Kalpathy-Cramer J, Succi MD. Automated tracking of emergency department abdominal CT findings during the COVID-19 pandemic using natural language processing. Am J Emerg Med. 2021 Nov; 49:52-57.
    View in: PubMed
    Score: 0.040
  6. Li MD, Deng F, Chang K, Kalpathy-Cramer J, Huang AJ. Automated Radiology-Arthroscopy Correlation of Knee Meniscal Tears Using Natural Language Processing Algorithms. Acad Radiol. 2022 04; 29(4):479-487.
    View in: PubMed
    Score: 0.039
  7. Müller H, Kalpathy-Cramer J, Hersh W, Geissbuhler A. Using medline queries to generate image retrieval tasks for benchmarking. Stud Health Technol Inform. 2008; 136:523-8.
    View in: PubMed
    Score: 0.016
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.

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