Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology
- 1 December 2013
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 169-176
- https://doi.org/10.1109/iccv.2013.28
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.Keywords
This publication has 27 references indexed in Scilit:
- Integrated profiling of three dimensional cell culture models and 3D microscopyBioinformatics, 2013
- Classification of Tumor Histology via Morphometric ContextPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Classification of tumor histopathology via sparse feature learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular AssociationIEEE Transactions on Medical Imaging, 2012
- Biological interpretation of morphological patterns in histopathological whole-slide imagesPublished by Association for Computing Machinery (ACM) ,2012
- Coupled Analysis of In Vitro and Histology Tissue Samples to Quantify Structure-Function RelationshipPLOS ONE, 2012
- Multiscale Feature Analysis of Salivary Gland Branching MorphogenesisPLOS ONE, 2012
- Time-efficient sparse analysis of histopathological whole slide imagesComputerized Medical Imaging and Graphics, 2011
- Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene CategoriesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Histologic Grading of Breast Cancer: Linkage of Patient Outcome with Level of Pathologist AgreementLaboratory Investigation, 2000