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Deep Learning in Label-free Cell Classification

Overview of attention for article published in Scientific Reports, January 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
19 news outlets
blogs
6 blogs
twitter
165 tweeters
facebook
1 Facebook page
wikipedia
3 Wikipedia pages
googleplus
2 Google+ users

Citations

dimensions_citation
88 Dimensions

Readers on

mendeley
356 Mendeley
citeulike
1 CiteULike
Title
Deep Learning in Label-free Cell Classification
Published in
Scientific Reports, January 2016
DOI 10.1038/srep21471
Pubmed ID
Authors

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi, Bahram Jalali, Chen, Claire Lifan, Mahjoubfar, Ata, Tai, Li-Chia, Blaby, Ian K, Huang, Allen, Niazi, Kayvan Reza, Jalali, Bahram

Abstract

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Twitter Demographics

The data shown below were collected from the profiles of 165 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 356 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 4 1%
Germany 3 <1%
Canada 3 <1%
United Kingdom 2 <1%
United States 2 <1%
Denmark 2 <1%
Brazil 1 <1%
Switzerland 1 <1%
Chile 1 <1%
Other 1 <1%
Unknown 336 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 106 30%
Researcher 62 17%
Student > Master 57 16%
Student > Bachelor 39 11%
Professor > Associate Professor 22 6%
Other 70 20%
Readers by discipline Count As %
Engineering 94 26%
Computer Science 68 19%
Agricultural and Biological Sciences 51 14%
Biochemistry, Genetics and Molecular Biology 34 10%
Physics and Astronomy 34 10%
Other 75 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 285. 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 19 April 2018.
All research outputs
#36,140
of 12,375,162 outputs
Outputs from Scientific Reports
#462
of 56,193 outputs
Outputs of similar age
#1,665
of 273,391 outputs
Outputs of similar age from Scientific Reports
#27
of 3,033 outputs
Altmetric has tracked 12,375,162 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 56,193 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.2. 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 273,391 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 99% of its contemporaries.
We're also able to compare this research output to 3,033 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 99% of its contemporaries.