Classification of histology sections via multispectral convolutional sparse coding

Yin Zhou, Hang Chang, Kenneth Barner, Paul Spellman, Bahram Parvin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

64 Citations (Scopus)

Abstract

Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSDSPM [5]), 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM [4]).

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages3081-3088
Number of pages8
ISBN (Print)9781479951178, 9781479951178
DOIs
StatePublished - Sep 24 2014
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period6/23/146/28/14

Fingerprint

Histology
Filter banks
Convolution
Tumors
Microscopes
Visualization
Genes
Tissue
Imaging techniques

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Zhou, Y., Chang, H., Barner, K., Spellman, P., & Parvin, B. (2014). Classification of histology sections via multispectral convolutional sparse coding. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3081-3088). [6909790] IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.394

Classification of histology sections via multispectral convolutional sparse coding. / Zhou, Yin; Chang, Hang; Barner, Kenneth; Spellman, Paul; Parvin, Bahram.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. p. 3081-3088 6909790.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, Y, Chang, H, Barner, K, Spellman, P & Parvin, B 2014, Classification of histology sections via multispectral convolutional sparse coding. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., 6909790, IEEE Computer Society, pp. 3081-3088, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 6/23/14. https://doi.org/10.1109/CVPR.2014.394
Zhou Y, Chang H, Barner K, Spellman P, Parvin B. Classification of histology sections via multispectral convolutional sparse coding. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society. 2014. p. 3081-3088. 6909790 https://doi.org/10.1109/CVPR.2014.394
Zhou, Yin ; Chang, Hang ; Barner, Kenneth ; Spellman, Paul ; Parvin, Bahram. / Classification of histology sections via multispectral convolutional sparse coding. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. pp. 3081-3088
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