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Parallel multiple instance learning for extremely large histopathology image analysis

Overview of attention for article published in BMC Bioinformatics, August 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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1 X user
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1 patent

Citations

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19 Dimensions

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65 Mendeley
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Title
Parallel multiple instance learning for extremely large histopathology image analysis
Published in
BMC Bioinformatics, August 2017
DOI 10.1186/s12859-017-1768-8
Pubmed ID
Authors

Yan Xu, Yeshu Li, Zhengyang Shen, Ziwei Wu, Teng Gao, Yubo Fan, Maode Lai, Eric I-Chao Chang

Abstract

Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 23%
Researcher 7 11%
Student > Master 5 8%
Student > Bachelor 4 6%
Student > Postgraduate 4 6%
Other 10 15%
Unknown 20 31%
Readers by discipline Count As %
Computer Science 16 25%
Engineering 8 12%
Medicine and Dentistry 6 9%
Biochemistry, Genetics and Molecular Biology 3 5%
Agricultural and Biological Sciences 2 3%
Other 8 12%
Unknown 22 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 April 2022.
All research outputs
#7,475,276
of 23,493,900 outputs
Outputs from BMC Bioinformatics
#2,915
of 7,397 outputs
Outputs of similar age
#117,124
of 318,551 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 92 outputs
Altmetric has tracked 23,493,900 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,397 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 59% 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 318,551 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 92 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 61% of its contemporaries.