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Automatic detection of anomalies in screening mammograms

Overview of attention for article published in BMC Medical Imaging, December 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#45 of 470)
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

twitter
11 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
34 Mendeley
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Title
Automatic detection of anomalies in screening mammograms
Published in
BMC Medical Imaging, December 2013
DOI 10.1186/1471-2342-13-43
Pubmed ID
Authors

Edward J Kendall, Michael G Barnett, Krista Chytyk-Praznik

Abstract

Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 3%
Unknown 33 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 15%
Student > Master 5 15%
Researcher 5 15%
Student > Ph. D. Student 3 9%
Student > Postgraduate 2 6%
Other 6 18%
Unknown 8 24%
Readers by discipline Count As %
Computer Science 7 21%
Medicine and Dentistry 7 21%
Engineering 6 18%
Nursing and Health Professions 2 6%
Mathematics 2 6%
Other 0 0%
Unknown 10 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 February 2014.
All research outputs
#3,853,322
of 19,510,965 outputs
Outputs from BMC Medical Imaging
#45
of 470 outputs
Outputs of similar age
#53,886
of 289,132 outputs
Outputs of similar age from BMC Medical Imaging
#3
of 39 outputs
Altmetric has tracked 19,510,965 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 470 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done particularly well, scoring higher than 90% 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 289,132 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.