Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching

Hang Chang, Nandita Nayak, Paul T. Spellman, Bahram Parvin

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

27 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages91-98
Number of pages8
EditionPART 2
DOIs
StatePublished - Oct 24 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8150 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/26/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Chang, H., Nayak, N., Spellman, P. T., & Parvin, B. (2013). Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 2 ed., pp. 91-98). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-40763-5_12