Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium

Lydia J. Wilson; Frederico C. Kiffer; Daniel C. Berrios; Abigail Bryce-Atkinson; Sylvain V. Costes; Olivier Gevaert; Bruno F.E. Matarèse; Jack Miller; Pritam Mukherjee; Kristen Peach; Paul N. Schofield; Luke T. Slater; Britta Langen

doi:10.1080/09553002.2023.2173823

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium

Lydia J. Wilson, Frederico C. Kiffer, Daniel C. Berrios, Abigail Bryce-Atkinson, Sylvain V. Costes, Olivier Gevaert, Bruno F.E. Matarèse, Jack Miller, Pritam Mukherjee, Kristen Peach, Paul N. Schofield, Luke T. Slater, Britta Langen

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3 Scopus citations

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 67^th 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.

Original language	English (US)
Pages (from-to)	1291-1300
Number of pages	10
Journal	International Journal of Radiation Biology
Volume	99
Issue number	8
DOIs	https://doi.org/10.1080/09553002.2023.2173823
State	Published - 2023

Keywords

artificial intelligence
lung cancer
Machine learning
ontology
radiobiology
radiotherapy
voxel-based analysis

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging

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10.1080/09553002.2023.2173823

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Wilson, L. J., Kiffer, F. C., Berrios, D. C., Bryce-Atkinson, A., Costes, S. V., Gevaert, O., Matarèse, B. F. E., Miller, J., Mukherjee, P., Peach, K., Schofield, P. N., Slater, L. T., & Langen, B. (2023). Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium. International Journal of Radiation Biology, 99(8), 1291-1300. https://doi.org/10.1080/09553002.2023.2173823

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 2023, 'Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium', International Journal of Radiation Biology, vol. 99, no. 8, pp. 1291-1300. https://doi.org/10.1080/09553002.2023.2173823

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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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year = "2023",

doi = "10.1080/09553002.2023.2173823",

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Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium

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Keywords

ASJC Scopus subject areas

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