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A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images

Overview of attention for article published in Journal of Digital Imaging, November 2018
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1 X user

Citations

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Readers on

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120 Mendeley
Title
A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images
Published in
Journal of Digital Imaging, November 2018
DOI 10.1007/s10278-018-0160-1
Pubmed ID
Authors

Ruba Alkadi, Fatma Taher, Ayman El-baz, Naoufel Werghi

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 120 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 13%
Student > Master 11 9%
Researcher 8 7%
Student > Bachelor 8 7%
Other 7 6%
Other 15 13%
Unknown 55 46%
Readers by discipline Count As %
Computer Science 26 22%
Medicine and Dentistry 15 13%
Engineering 7 6%
Biochemistry, Genetics and Molecular Biology 3 3%
Agricultural and Biological Sciences 1 <1%
Other 5 4%
Unknown 63 53%
Attention Score in Context

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 31 July 2019.
All research outputs
#20,564,621
of 23,140,503 outputs
Outputs from Journal of Digital Imaging
#951
of 1,070 outputs
Outputs of similar age
#372,587
of 437,226 outputs
Outputs of similar age from Journal of Digital Imaging
#24
of 26 outputs
Altmetric has tracked 23,140,503 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 1,070 research outputs from this source. They receive a mean Attention Score of 4.6. 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 437,226 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 26 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.