Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium Article Swipe
Lydia Wilson
,
Frederico Kiffer
,
Daniel C. Berrios
,
Abigail Bryce-Atkinson
,
Sylvain V. Costes
,
Olivier Gevaert
,
Bruno F. E. Matarèse
,
J. Miller
,
Pritam Mukherjee
,
Kristen Peach
,
Paul N. Schofield
,
Luke T. Slater
,
Britta Langen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/09553002.2023.2173823
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/09553002.2023.2173823
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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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1080/09553002.2023.2173823
- OA Status
- hybrid
- Cited By
- 4
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1080/09553002.2023.2173823Digital Object Identifier
- Title
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Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposiumWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-02-03Full publication date if available
- Authors
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Lydia Wilson, Frederico Kiffer, Daniel C. Berrios, Abigail Bryce-Atkinson, Sylvain V. Costes, Olivier Gevaert, Bruno F. E. Matarèse, J. Miller, Pritam Mukherjee, Kristen Peach, Paul N. Schofield, Luke T. Slater, Britta LangenList of authors in order
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https://doi.org/10.1080/09553002.2023.2173823Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1080/09553002.2023.2173823Direct OA link when available
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Radiation oncology, Big data, Metadata, Data science, Medical physics, Computer science, Medicine, Radiation therapy, Data mining, Radiology, World Wide WebTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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