Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.
Author
Wilson, Lydia JKiffer, 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
Publication date
2023-02-06
Metadata
Show full item recordAbstract
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.Citation
Wilson LJ, Kiffer FC, Berrios DC, Bryce-Atkinson A, Costes SV, Gevaert O, Matarèse BFE, Miller J, Mukherjee P, Peach K, Schofield PN, Slater LT, Langen B. Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium. Int J Radiat Biol. 2023;99(8):1291-1300. doi: 10.1080/09553002.2023.2173823. Epub 2023 Feb 6Type
ArticleAdditional Links
http://www.tandfonline.com/loi/irab20PMID
36735963Publisher
Taylor and Francis Groupae974a485f413a2113503eed53cd6c53
10.1080/09553002.2023.2173823