Learning invariant features of tumor signatures

Quoc V. Le, Ju Han, Joe W. Gray, Paul T. Spellman, Alexander Borowsky, Bahram Parvin

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

26 Scopus citations


We present a novel method for automated learning of features from unlabeled image patches for classification of tumor architecture. In contrast to previous manually-designed feature detectors (e.g., Gabor basis function), the proposed method utilizes inexpensive un-labeled data to construct features. The algorithm, also known as reconstruction independent subspace analysis, can be described as a two-layer network with non-linear responses, where the second layer represents subspace structures. The technique is applied to tissue sections for characterizing necrosis, apoptotic, and viable regions of Glioblastoma Multifrome (GBM) from TCGA dataset. Experimental results show that this method outperforms more complex expert-designed approaches. The fact that our approach learns features automatically from unlabeled data promises a wider application of self-learning strategies for tissue characterization.

Original languageEnglish (US)
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Number of pages4
StatePublished - 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012


  • apoptotic and necrotic signatures
  • subspace learning
  • tumor architecture

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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