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Machine Intelligence for Radiation Science: Summary of the Radiation Research Society 67th Annual Meeting Symposium 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.
Document ID
20220011572
Acquisition Source
Ames Research Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Lydia J. Wilson ORCID
(St. Jude Children's Research Hospital Memphis, Tennessee, United States)
Frederico C. Kiffer ORCID
(Children's Hospital of Philadelphia)
Daniel C. Berrios ORCID
(Ames Research Center Mountain View, California, United States)
Abigail Bryce-Atkinson
(University of Manchester Manchester, Manchester, United Kingdom)
Sylvain V. Costes ORCID
(Ames Research Center Mountain View, California, United States)
Olivier Gevaert
(Stanford University Stanford, California, United States)
Bruno F. E. Matarese ORCID
(University of Cambridge Cambridge, United Kingdom)
Jack Miller ORCID
(KBR (United States) Houston, Texas, United States)
Pritam Mukherjee ORCID
(Stanford University Stanford, California, United States)
Kristen Peach ORCID
(Bionetics (United States) Yorktown, Virginia, United States)
Paul N. Schofield ORCID
(University of Cambridge Cambridge, United Kingdom)
Luke T. Slater ORCID
(University of Birmingham Birmingham, United Kingdom)
Britta Langen ORCID
(The University of Texas Southwestern Medical Center Dallas, Texas, United States)
Date Acquired
July 30, 2022
Publication Date
February 6, 2023
Publication Information
Publication: International Journal of Radiation Biology
Publisher: Taylor and Francis
Volume: 99
Issue: 8
Issue Publication Date: January 1, 2023
ISSN: 0955-3002
e-ISSN: 1362-3095
Subject Category
Computer Programming And Software
Life Sciences (General)
Physics Of Elementary Particles And Fields
Funding Number(s)
CONTRACT_GRANT: NNA14AB82C
CONTRACT_GRANT: EURATOM 900009
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
External Peer Committee
Keywords
radiation
ontology
knowledge representation
taxonomies
vocabularies
artificial intelligence
analytics
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