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Differentiating non-lactating mastitis and malignant breast tumors by deep-learning based AI automatic classification system: A preliminary study

Overview of attention for article published in Frontiers in oncology, September 2022
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Mentioned by

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1 X user

Citations

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6 Dimensions

Readers on

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13 Mendeley
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Title
Differentiating non-lactating mastitis and malignant breast tumors by deep-learning based AI automatic classification system: A preliminary study
Published in
Frontiers in oncology, September 2022
DOI 10.3389/fonc.2022.997306
Pubmed ID
Authors

Ying Zhou, Bo-Jian Feng, Wen-Wen Yue, Yuan Liu, Zhi-Feng Xu, Wei Xing, Zhao Xu, Jin-Cao Yao, Shu-Rong Wang, Dong Xu

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 8%
Researcher 1 8%
Lecturer 1 8%
Unknown 10 77%
Readers by discipline Count As %
Unspecified 1 8%
Computer Science 1 8%
Medicine and Dentistry 1 8%
Unknown 10 77%
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 10 September 2023.
All research outputs
#21,316,883
of 26,179,695 outputs
Outputs from Frontiers in oncology
#11,669
of 22,919 outputs
Outputs of similar age
#323,730
of 437,349 outputs
Outputs of similar age from Frontiers in oncology
#1,048
of 1,803 outputs
Altmetric has tracked 26,179,695 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,919 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 28th percentile – i.e., 28% 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 437,349 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,803 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.