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Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Overview of attention for article published in Frontiers in oncology, March 2021
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1 X user

Citations

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76 Mendeley
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Title
Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model
Published in
Frontiers in oncology, March 2021
DOI 10.3389/fonc.2021.623506
Pubmed ID
Authors

Xianyu Zhang, Hui Li, Chaoyun Wang, Wen Cheng, Yuntao Zhu, Dapeng Li, Hui Jing, Shu Li, Jiahui Hou, Jiaying Li, Yingpu Li, Yashuang Zhao, Hongwei Mo, Da Pang

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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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 11%
Student > Bachelor 6 8%
Researcher 4 5%
Student > Master 4 5%
Professor > Associate Professor 3 4%
Other 12 16%
Unknown 39 51%
Readers by discipline Count As %
Computer Science 15 20%
Engineering 7 9%
Biochemistry, Genetics and Molecular Biology 3 4%
Social Sciences 2 3%
Medicine and Dentistry 2 3%
Other 6 8%
Unknown 41 54%
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 22 March 2021.
All research outputs
#23,487,873
of 26,163,973 outputs
Outputs from Frontiers in oncology
#16,353
of 22,911 outputs
Outputs of similar age
#397,093
of 457,123 outputs
Outputs of similar age from Frontiers in oncology
#679
of 971 outputs
Altmetric has tracked 26,163,973 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 22,911 research outputs from this source. They receive a mean Attention Score of 3.1. 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 457,123 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 971 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.