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Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks

Overview of attention for article published in Scientific Reports, May 2018
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  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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2 tweeters

Citations

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66 Dimensions

Readers on

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77 Mendeley
Title
Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
Published in
Scientific Reports, May 2018
DOI 10.1038/s41598-018-25842-6
Pubmed ID
Authors

Hirotoshi Takiyama, Tsuyoshi Ozawa, Soichiro Ishihara, Mitsuhiro Fujishiro, Satoki Shichijo, Shuhei Nomura, Motoi Miura, Tomohiro Tada

Abstract

The use of convolutional neural networks (CNNs) has dramatically advanced our ability to recognize images with machine learning methods. We aimed to construct a CNN that could recognize the anatomical location of esophagogastroduodenoscopy (EGD) images in an appropriate manner. A CNN-based diagnostic program was constructed based on GoogLeNet architecture, and was trained with 27,335 EGD images that were categorized into four major anatomical locations (larynx, esophagus, stomach and duodenum) and three subsequent sub-classifications for stomach images (upper, middle, and lower regions). The performance of the CNN was evaluated in an independent validation set of 17,081 EGD images by drawing receiver operating characteristics (ROC) curves and calculating the area under the curves (AUCs). ROC curves showed high performance of the trained CNN to classify the anatomical location of EGD images with AUCs of 1.00 for larynx and esophagus images, and 0.99 for stomach and duodenum images. Furthermore, the trained CNN could recognize specific anatomical locations within the stomach, with AUCs of 0.99 for the upper, middle, and lower stomach. In conclusion, the trained CNN showed robust performance in its ability to recognize the anatomical location of EGD images, highlighting its significant potential for future application as a computer-aided EGD diagnostic system.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 11 14%
Researcher 11 14%
Student > Ph. D. Student 10 13%
Student > Master 9 12%
Student > Postgraduate 4 5%
Other 13 17%
Unknown 19 25%
Readers by discipline Count As %
Computer Science 17 22%
Medicine and Dentistry 17 22%
Engineering 9 12%
Agricultural and Biological Sciences 5 6%
Nursing and Health Professions 1 1%
Other 5 6%
Unknown 23 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 15 May 2018.
All research outputs
#7,802,785
of 12,942,450 outputs
Outputs from Scientific Reports
#32,832
of 61,254 outputs
Outputs of similar age
#150,071
of 270,335 outputs
Outputs of similar age from Scientific Reports
#11
of 28 outputs
Altmetric has tracked 12,942,450 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 61,254 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.4. This one is in the 41st percentile – i.e., 41% 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 270,335 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.