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Rethinking Skin Lesion Segmentation in a Convolutional Classifier

Overview of attention for article published in Journal of Digital Imaging, October 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 574)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
6 news outlets
twitter
8 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
26 Mendeley
Title
Rethinking Skin Lesion Segmentation in a Convolutional Classifier
Published in
Journal of Digital Imaging, October 2017
DOI 10.1007/s10278-017-0026-y
Pubmed ID
Authors

Jack Burdick, Oge Marques, Janet Weinthal, Borko Furht

Abstract

Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore unclear, previous works have found image segmentation to be, conflictingly, both detrimental and beneficial to skin lesion classification. We investigate the effect of expanding the segmentation border to include pixels surrounding the target lesion. Ostensibly, segmenting a target skin lesion will remove inessential information, non-lesion skin, and artifacts to aid in classification. Our results indicate that segmentation border enlargement produces, to a certain degree, better results across all metrics of interest when using a convolutional based classifier built using the transfer learning paradigm. Consequently, preprocessing methods which produce borders larger than the actual lesion can potentially improve classifier performance, more than both perfect segmentation, using dermatologist created ground truth masks, and no segmentation altogether.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 38%
Student > Ph. D. Student 6 23%
Researcher 4 15%
Professor 2 8%
Unspecified 2 8%
Other 2 8%
Readers by discipline Count As %
Computer Science 8 31%
Medicine and Dentistry 7 27%
Engineering 6 23%
Unspecified 3 12%
Neuroscience 1 4%
Other 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 50. 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 13 December 2017.
All research outputs
#288,467
of 12,292,436 outputs
Outputs from Journal of Digital Imaging
#4
of 574 outputs
Outputs of similar age
#14,243
of 288,822 outputs
Outputs of similar age from Journal of Digital Imaging
#1
of 23 outputs
Altmetric has tracked 12,292,436 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 574 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 99% 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 288,822 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 23 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 95% of its contemporaries.