Stacked predictive sparse coding for classification of distinct regions in tumor histopathology

Hang Chang, Yin Zhou, Paul Spellman, Bahram Parvin

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

22 Citations (Scopus)

Abstract

Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for tumor composition. Furthermore, aggregation of these indices, from each whole slide image (WSI) in a large cohort, can provide predictive models of the clinical outcome. However, performance of the existing techniques is hindered as a result of large technical variations and biological heterogeneities that are always present in a large cohort. We propose a system that automatically learns a series of basis functions for representing the underlying spatial distribution using stacked predictive sparse decomposition (PSD). The learned representation is then fed into the spatial pyramid matching framework (SPM) with a linear SVM classifier. The system has been evaluated for classification of (a) distinct histological components for two cohorts of tumor types, and (b) colony organization of normal and malignant cell lines in 3D cell culture models. Throughput has been increased through the utility of graphical processing unit (GPU), and evaluation indicates a superior performance results, compared with previous research.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages169-176
Number of pages8
ISBN (Print)9781479928392
DOIs
StatePublished - 2013
Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: Dec 1 2013Dec 8 2013

Other

Other2013 14th IEEE International Conference on Computer Vision, ICCV 2013
CountryAustralia
CitySydney, NSW
Period12/1/1312/8/13

Fingerprint

Tumors
Histology
Cell culture
Spatial distribution
Classifiers
Agglomeration
Cells
Throughput
Decomposition
Processing
Chemical analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Chang, H., Zhou, Y., Spellman, P., & Parvin, B. (2013). Stacked predictive sparse coding for classification of distinct regions in tumor histopathology. In Proceedings of the IEEE International Conference on Computer Vision (pp. 169-176). [6751130] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2013.28

Stacked predictive sparse coding for classification of distinct regions in tumor histopathology. / Chang, Hang; Zhou, Yin; Spellman, Paul; Parvin, Bahram.

Proceedings of the IEEE International Conference on Computer Vision. Institute of Electrical and Electronics Engineers Inc., 2013. p. 169-176 6751130.

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

Chang, H, Zhou, Y, Spellman, P & Parvin, B 2013, Stacked predictive sparse coding for classification of distinct regions in tumor histopathology. in Proceedings of the IEEE International Conference on Computer Vision., 6751130, Institute of Electrical and Electronics Engineers Inc., pp. 169-176, 2013 14th IEEE International Conference on Computer Vision, ICCV 2013, Sydney, NSW, Australia, 12/1/13. https://doi.org/10.1109/ICCV.2013.28
Chang H, Zhou Y, Spellman P, Parvin B. Stacked predictive sparse coding for classification of distinct regions in tumor histopathology. In Proceedings of the IEEE International Conference on Computer Vision. Institute of Electrical and Electronics Engineers Inc. 2013. p. 169-176. 6751130 https://doi.org/10.1109/ICCV.2013.28
Chang, Hang ; Zhou, Yin ; Spellman, Paul ; Parvin, Bahram. / Stacked predictive sparse coding for classification of distinct regions in tumor histopathology. Proceedings of the IEEE International Conference on Computer Vision. Institute of Electrical and Electronics Engineers Inc., 2013. pp. 169-176
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