Predictive sparse morphometric context for classification of histology sections

Hang Chang, Paul Spellman, Bahram Parvin

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


Classification of histology sections from large cohorts, in terms of distinct regions of microanatomy (e.g., tumor, stroma, normal), enables the quantification of tumor composition, and the construction of predictive models of the clinical outcome. To tackle the batch effects and biological heterogeneities that are persistent in large cohorts, sparse cellular morphometric context has recently been developed for invariant representation of the underlying properties in the data, which summarizes cellular morphometric features at various locations and scales, and leads to a system with superior performance for classification of microanatomy and histopathology. However, the sparse optimization protocol for the calculation of sparse cellular morphometric features is not scalable for large scale classification. To improve the scalability of systems, based on sparse morphometric context, we propose the predictive sparse morphometric context in place of the original implementation, which approximates the sparse cellular morphometric feature through a non-linear regressor that is jointly learned with an over-complete dictionary in an unsupervised manner. Experimental results indicates over 50 times speedup compared to our previous implementation with the help of non-linear regressor; while producing competitive performance.

Original languageEnglish (US)
Title of host publicationProceedings - International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages4
ISBN (Print)9781479923748
Publication statusPublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015


Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States



  • Classification
  • H&E Tissue Section
  • Sparse Coding

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Chang, H., Spellman, P., & Parvin, B. (2015). Predictive sparse morphometric context for classification of histology sections. In Proceedings - International Symposium on Biomedical Imaging (Vol. 2015-July, pp. 1004-1007). [7164040] IEEE Computer Society.