Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications
European Journal of Nuclear Medicine and Molecular Imaging, ISSN: 1619-7089, Vol: 46, Issue: 13, Page: 2630-2637
2019
- 111Citations
- 231Captures
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations111
- Citation Indexes111
- 111
- CrossRef14
- Captures231
- Readers231
- 231
- Mentions1
- News Mentions1
- News1
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Review Description
Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstruction, processing (denoising, segmentation), analysis and predictive modelling. In this review we introduce and define these key concepts and discuss how the techniques from this field can be applied to nuclear medicine imaging applications with a particular focus on radio(geno)mics.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85068831396&origin=inward; http://dx.doi.org/10.1007/s00259-019-04373-w; http://www.ncbi.nlm.nih.gov/pubmed/31280350; http://link.springer.com/10.1007/s00259-019-04373-w; https://dx.doi.org/10.1007/s00259-019-04373-w; https://link.springer.com/article/10.1007/s00259-019-04373-w
Springer Science and Business Media LLC
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