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Characterization of Tissue Histopathology via Predictive Sparse Decomposition and Spatial Pyramid Matching

  • Hang Chang
  • Nandita Nayak
  • Paul T. Spellman
  • Bahram Parvin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Image-based classification of tissue histology, in terms of different components (e.g., subtypes of aberrant phenotypic signatures), provides a set of indices for tumor composition. Subsequently, integration of these indices in whole slide images (WSI), from a large cohort, can provide predictive models of the clinical outcome. However, the performance of the existing histology-based classification techniques is hindered as a result of large technical and biological variations that are always present in a large cohort. In this paper, we propose an algorithm for classification of tissue histology based on predictive sparse decomposition (PSD) and spatial pyramid matching (SPM), which utilize sparse tissue morphometric signatures at various locations and scales. The method has been evaluated on two distinct datasets of different tumor types collected from The Cancer Genome Atlas (TCGA). The novelties of our approach are: (i) extensibility to different tumor types; (ii) robustness in the presence of wide technical and biological variations; and (iii) scalability with varying training sample size.

Keywords

Renal Clear Cell Carcinoma Linear Support Vector Machine Whole Slide Image Spatial Pyramid Match Distinct Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hang Chang
    • 1
  • Nandita Nayak
    • 1
  • Paul T. Spellman
    • 2
  • Bahram Parvin
    • 1
  1. 1.Life Sciences DivisionLawrence Berkeley National LaboratoryBerkeleyUS
  2. 2.Center for Spatial Systems BiomedicineOregon Health Sciences UniversityPortlandUS

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