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Machine learning: from radiomics to discovery and routine

Overview of attention for article published in Die Radiologie, June 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#3 of 329)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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50 X users

Citations

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38 Dimensions

Readers on

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122 Mendeley
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Title
Machine learning: from radiomics to discovery and routine
Published in
Die Radiologie, June 2018
DOI 10.1007/s00117-018-0407-3
Pubmed ID
Authors

G. Langs, S. Röhrich, J. Hofmanninger, F. Prayer, J. Pan, C. Herold, H. Prosch

Abstract

Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology.

X Demographics

X Demographics

The data shown below were collected from the profiles of 50 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 122 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 15%
Researcher 18 15%
Student > Master 15 12%
Student > Bachelor 10 8%
Student > Doctoral Student 9 7%
Other 17 14%
Unknown 35 29%
Readers by discipline Count As %
Medicine and Dentistry 29 24%
Computer Science 13 11%
Engineering 7 6%
Biochemistry, Genetics and Molecular Biology 6 5%
Physics and Astronomy 3 2%
Other 18 15%
Unknown 46 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 31. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 October 2019.
All research outputs
#1,316,802
of 25,795,662 outputs
Outputs from Die Radiologie
#3
of 329 outputs
Outputs of similar age
#27,439
of 342,805 outputs
Outputs of similar age from Die Radiologie
#1
of 6 outputs
Altmetric has tracked 25,795,662 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 329 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,805 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them