You are here

  • Lu C, Browne AC, Allen C, Divita G. Using Lexical tools to convert Unicode characters to ASCII AMIA Annu Symp Proc. 2008 Nov 6:1031.
  • Hristovski D, Kastrin A, Dinevski D, Burgun A, Ziberna L, Rindflesch TC. Using Literature-Based Discovery to Explain Adverse Drug Effects. J Med Syst. 2016 Aug;40(8):185. doi: 10.1007/s10916-016-0544-z. Epub 2016 Jun 18.
  • Hristovski D, Peterlin B, Mitchell JA, Humphrey SM. Using literature-based discovery to identify disease candidate genes. Int J Med Inform. March 2005;74(2-4):289-98.
  • Hristovski D, Rindflesch TC, Peterlin B. Using literature-based discovery to identify novel therapeutic approaches. Cardiovasc Hematol Agents Med Chem. 2013 Mar;11(1):14-24.
  • Libbus B, Kilicoglu H, Rindflesch TC, Mork JG, Aronson AR. Using Natural Languge Processing, Locus Link, and the Gene Ontology to Compare OMIM to MEDLINE Proceedings of the HLT-NAACL Workshop on Linking the Biological Literature, Ontologies and Databases: Tools for Users. 2004;:69-76.
  • Xu X, Lee DJ, Antani SK, Long LR, Archiband JK. Using Relevance Feedback with Short-term Memory for Content-based Spine X-ray Image Retrieval. J Neurocomputing. June 2009;72(10-12):2259-69.
  • Zhang R, Cairelli MJ, Fiszman M, Rosemblat G, Kilicoglu H, Rindflesch TC, Pakhomov SV, Melton GB. Using semantic predications to uncover drug-drug interactions in clinical data. J Biomed Inform. 2014 Jun;49:134-47. doi: 10.1016/j.jbi.2014.01.004. Epub 2014 Jan 19.
  • Zhang G-Q, Bodenreider O. Using SPARQL to Test for Lattices: Application to Quality Assurance in Biomedical Ontologies In: Patel-Schneider PF, Pan Y, Hitzler P, Mika P, Zhang L, Pan JZ, et al., editors. Proceedings of the 9th International Semantic Web Conference (ISWC 2010), Shanghai, China, November 7-11, 2010. Berlin, Heidelberg: Springer; 2010. p. 273-288.
  • Leroy G, Rindflesch TC. Using Symbolic Knowledge in the UMLS to Disambiguate Words in Small Datasets with a Naive Bayes Classifier Medinfo. 2004 Sept.;2004: 381-385.
  • Jimeno Yepes A, Mork JG, Aronson A. Using the argumentative structure of scientific literature to improve information access [Poster]. BioNLP 2013.
  • Ahlers CB, Hristovski D, Kilicoglu H, Rindflesch TC. Using the Literature-based Discovery Paradigm to Investigate Drug Mechanisms AMIA Annu Symp Proc. 2007 Oct 11:6-10
  • Rondonotti E, Koulaouzidis A, Karargyris A, Giannakou A, Fini L, Soncini M, Pennazio M, Douglas S, Shams A, Lachlan N, Zahid A, Mandelli G, Girelli C. Utility of 3-dimensional image reconstruction in the diagnosis of small-bowel masses in capsule endoscopy (with video). Gastrointest Endosc. 2014 Oct;80(4):642-51. doi: 10.1016/j.gie.2014.04.057. Epub 2014 Jul 3.
  • Antani S, Long LR, Thoma GR, Stanley RJ. Vertebra Shape Classification using MLP for Content-Based Image Retrieval International Neural Networks Society and IEEE Neural Networks Society. 2003 July 2003;:160-65.
  • Ducut E, Liu F, Avila JM, Encinas MA, Diwa M, Fontelo P. Virtual Microscopy in a Developing Country: A Collaborative Approach to Building an Image Library. Journal of eHealth Technology and Application. 2010 Sep;8(2):112-5.
  • Fontelo P, DiNino E, Johansen K, Khan A, Ackerman MJ. Virtual Microscopy: Potential Applications in Medical Education and Telemedicine in Countries with Developing Economies. Proceedings of the 38th Hawaii International Conference on System Sciences; 2005 Jan 3-6; Big Island, Hawaii: 7 pages. IEEE Computer Society.
  • Ratiu P, Hillen B, Glaser J, Jenkins DB. Visible Human 2.0 - The Next Generation. In: Westwood JD, Hoffman HM, Mogel GT, Phillips R, Robb RA, Stredney D, editors. Stud Health Technol Inform [Studies in Health Technology and Informatics] -- Proceedings of the 11th annual Medicine Meets Virtual Reality conference; 2003 Jan;94:275-81. Amsterdam: IOS Press.
  • Ackerman MJ. Visible Human Project. McGraw-Hill 2004 Yearbook of Science & Technology. New York: McGraw-Hill. 2004. p. 369-72.
  • Ackerman MJ. Visible Human Project: From Data to Knowledge. In: Haux R, Kulikowski C, editors. Yearbook of Medical Informatics 2002: Medical Imaging Informatics. International Medical Informatics Association (IMIA). p. 115-7.
  • Jeronimo J, Massad LS, Schiffman M for the NIH-ASCCP Research Group. Visual Appearance of the Uterine Cervix: Correlation with Human Papillomavirus Detection and Type Am J Obstet Gynecol. 2007 Jul;197(1):47.e1-8
  • Kim I, Rajaraman S, Antani SK. Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities. Diagnostics (Basel). 2019 Apr 3;9(2). pii: E38. doi: 10.3390/diagnostics9020038.
  • Rajaraman S, Candemir S, Kim I, Thoma GR, Antani SK. Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs. Appl. Sci. 2018, 8, 1715.
  • Moorhead R, Johnson C, Munzner T, Pfister H, Rheingans P, Yoo TS. Visualization Corner: Visualization Research Challenges: A Report Summary. IEEE Computing in Science and Engineering. 2006 Jul-Aug;8(4):66-73. DOI: 10.1109/MCSE.2006.77.
  • Ozturk S, Kayaalp M, McDonald CJ. Visualization of patient prescription history data in emergency care. AMIA Annu Symp Proc. 2014 Nov 14;2014:963-8. eCollection 2014.
  • Munzner T, Johnson C, Moorhead R, Pfister H, Rheingans P, Yoo TS. Visualization Viewpoints: NIH-NSF Visualization Research Challenges Report Summary. IEEE Computer Graphics and Applications. 2006 Mar-Apr;26(2):20-24.
  • Rajaraman S, Antani SK, Xue Z, Candemir S, Jaeger S, Thoma GR. Visualizing abnormalities in chest radiographs through salient network activations in Deep Learning. Proc. IEEE Life Sciences Conference (LSC), Sydney, Australia, 2017. pp. 71-74, DOI:10.1109/LSC.2017.8268146.

Pages