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Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.
Wilson, Lydia J; Kiffer, Frederico C; Berrios, Daniel C; Bryce-Atkinson, Abigail; Costes, Sylvain V; Gevaert, Olivier; Matarèse, Bruno F E; Miller, Jack; Mukherjee, Pritam; Peach, Kristen; Schofield, Paul N; Slater, Luke T; Langen, Britta.
Afiliação
  • Wilson LJ; Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA.
  • Kiffer FC; Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA.
  • Berrios DC; NASA Ames Research Center, Moffett Field, CA, USA.
  • Bryce-Atkinson A; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Costes SV; NASA Ames Research Center, Moffett Field, CA, USA.
  • Gevaert O; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford, CA, USA.
  • Matarèse BFE; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA.
  • Miller J; The Cavendish Laboratory, University of Cambridge, Cambridge, UK.
  • Mukherjee P; Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
  • Peach K; NASA Ames Research Center, Moffett Field, CA, USA.
  • Schofield PN; KBR, NASA Ames Research Center, Moffett Field, CA, USA.
  • Slater LT; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford, CA, USA.
  • Langen B; Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, USA.
Int J Radiat Biol ; 99(8): 1291-1300, 2023.
Article em En | MEDLINE | ID: mdl-36735963
ABSTRACT
The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Neoplasias Pulmonares Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Neoplasias Pulmonares Idioma: En Ano de publicação: 2023 Tipo de documento: Article