title: Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching.
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.
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. hchang@lbl.gov
authors.forename: Hang
authors.initials: H
authors.lastname: Chang
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
authors.forename: Nandita
authors.initials: N
authors.lastname: Nayak
authors.affiliation: Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, OR, USA.
authors.forename: Paul T
authors.initials: PT
authors.lastname: Spellman
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
authors.forename: Bahram
authors.initials: B
authors.lastname: Parvin
mesh_terms: D000465:Algorithms; D001706:Biopsy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D009369:Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014407:Tumor Cells, Cultured