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Characterization of Tissue Histopathology via Predictive Sparse Decomposition and Spatial Pyramid Matching*
Hang Chang1, Nandita Nayak1, Paul T. Spellman2, and Bahram Parvin1
1Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, US
hchang@lbl.gov
b_parving@lbl.gov
2Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, OR, US
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.
*This work was supported by NIH grant U24 CA1437991 carried out at Lawrence Berkeley National Laboratory under Contract No. DE-AC02-05CH11231.
LNCS 8150, p. 91 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013