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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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.
期刊介绍：
The International Journal of Radiation Biology publishes original papers, reviews, current topic articles, technical notes/reports, and meeting reports on the effects of ionizing, UV and visible radiation, accelerated particles, electromagnetic fields, ultrasound, heat and related modalities. The focus is on the biological effects of such radiations: from radiation chemistry to the spectrum of responses of living organisms and underlying mechanisms, including genetic abnormalities, repair phenomena, cell death, dose modifying agents and tissue responses. Application of basic studies to medical uses of radiation extends the coverage to practical problems such as physical and chemical adjuvants which improve the effectiveness of radiation in cancer therapy. Assessment of the hazards of low doses of radiation is also considered.
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