↓ Skip to main content

Convolutional neural networks: an overview and application in radiology

Overview of attention for article published in Insights Into Imaging, June 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 526)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
24 tweeters

Citations

dimensions_citation
231 Dimensions

Readers on

mendeley
785 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Convolutional neural networks: an overview and application in radiology
Published in
Insights Into Imaging, June 2018
DOI 10.1007/s13244-018-0639-9
Pubmed ID
Authors

Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi

Abstract

Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care. • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm. • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

Twitter Demographics

The data shown below were collected from the profiles of 24 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 785 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 785 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 170 22%
Student > Bachelor 112 14%
Student > Ph. D. Student 111 14%
Researcher 58 7%
Student > Doctoral Student 34 4%
Other 122 16%
Unknown 178 23%
Readers by discipline Count As %
Computer Science 201 26%
Engineering 150 19%
Medicine and Dentistry 76 10%
Biochemistry, Genetics and Molecular Biology 21 3%
Physics and Astronomy 19 2%
Other 109 14%
Unknown 209 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 16 February 2019.
All research outputs
#752,270
of 15,223,481 outputs
Outputs from Insights Into Imaging
#16
of 526 outputs
Outputs of similar age
#24,548
of 276,598 outputs
Outputs of similar age from Insights Into Imaging
#1
of 25 outputs
Altmetric has tracked 15,223,481 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 526 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 96% 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 276,598 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 91% of its contemporaries.
We're also able to compare this research output to 25 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 96% of its contemporaries.