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Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder

Overview of attention for article published in BioMedical Engineering OnLine, May 2018
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Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
72 Mendeley
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Title
Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder
Published in
BioMedical Engineering OnLine, May 2018
DOI 10.1186/s12938-018-0496-2
Pubmed ID
Authors

Changmiao Wang, Ahmed Elazab, Fucang Jia, Jianhuang Wu, Qingmao Hu

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 18%
Student > Master 13 18%
Student > Ph. D. Student 10 14%
Student > Postgraduate 6 8%
Student > Bachelor 5 7%
Other 4 6%
Unknown 21 29%
Readers by discipline Count As %
Medicine and Dentistry 18 25%
Computer Science 9 13%
Engineering 8 11%
Nursing and Health Professions 2 3%
Physics and Astronomy 2 3%
Other 4 6%
Unknown 29 40%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 October 2018.
All research outputs
#12,224,665
of 13,810,416 outputs
Outputs from BioMedical Engineering OnLine
#486
of 592 outputs
Outputs of similar age
#165,602
of 200,132 outputs
Outputs of similar age from BioMedical Engineering OnLine
#16
of 22 outputs
Altmetric has tracked 13,810,416 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 592 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 200,132 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.