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Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors

Overview of attention for article published in BMC Bioinformatics, November 2016
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Title
Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1318-9
Pubmed ID
Authors

Yang Song, Weidong Cai, Heng Huang, Dagan Feng, Yue Wang, Mei Chen

Abstract

Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference. We evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis. We present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 21%
Professor > Associate Professor 3 16%
Student > Ph. D. Student 3 16%
Student > Bachelor 2 11%
Student > Doctoral Student 2 11%
Other 3 16%
Unknown 2 11%
Readers by discipline Count As %
Computer Science 5 26%
Engineering 4 21%
Biochemistry, Genetics and Molecular Biology 3 16%
Agricultural and Biological Sciences 2 11%
Psychology 1 5%
Other 0 0%
Unknown 4 21%

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 17 November 2016.
All research outputs
#6,587,047
of 8,647,833 outputs
Outputs from BMC Bioinformatics
#3,107
of 3,738 outputs
Outputs of similar age
#202,993
of 295,763 outputs
Outputs of similar age from BMC Bioinformatics
#91
of 128 outputs
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