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An active learning based classification strategy for the minority class problem: application to histopathology annotation

Overview of attention for article published in BMC Bioinformatics, October 2011
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Title
An active learning based classification strategy for the minority class problem: application to histopathology annotation
Published in
BMC Bioinformatics, October 2011
DOI 10.1186/1471-2105-12-424
Pubmed ID
Authors

Scott Doyle, James Monaco, Michael Feldman, John Tomaszewski, Anant Madabhushi

Abstract

Supervised classifiers for digital pathology can improve the ability of physicians to detect and diagnose diseases such as cancer. Generating training data for classifiers is problematic, since only domain experts (e.g. pathologists) can correctly label ground truth data. Additionally, digital pathology datasets suffer from the "minority class problem", an issue where the number of exemplars from the non-target class outnumber target class exemplars which can bias the classifier and reduce accuracy. In this paper, we develop a training strategy combining active learning (AL) with class-balancing. AL identifies unlabeled samples that are "informative" (i.e. likely to increase classifier performance) for annotation, avoiding non-informative samples. This yields high accuracy with a smaller training set size compared with random learning (RL). Previous AL methods have not explicitly accounted for the minority class problem in biomedical images. Pre-specifying a target class ratio mitigates the problem of training bias. Finally, we develop a mathematical model to predict the number of annotations (cost) required to achieve balanced training classes. In addition to predicting training cost, the model reveals the theoretical properties of AL in the context of the minority class problem.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 4 4%
Colombia 2 2%
Germany 1 <1%
Netherlands 1 <1%
France 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
United States 1 <1%
Unknown 102 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 18%
Researcher 17 15%
Student > Bachelor 11 10%
Student > Master 10 9%
Other 9 8%
Other 26 23%
Unknown 20 18%
Readers by discipline Count As %
Computer Science 33 29%
Medicine and Dentistry 15 13%
Engineering 13 11%
Agricultural and Biological Sciences 9 8%
Chemistry 6 5%
Other 16 14%
Unknown 22 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 October 2011.
All research outputs
#15,237,301
of 22,655,397 outputs
Outputs from BMC Bioinformatics
#5,353
of 7,236 outputs
Outputs of similar age
#95,964
of 140,785 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 99 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.