TY - GEN
T1 - Learning invariant features of tumor signatures
AU - Le, Quoc V.
AU - Han, Ju
AU - Gray, Joe W.
AU - Spellman, Paul T.
AU - Borowsky, Alexander
AU - Parvin, Bahram
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - apoptotic and necrotic signatures
KW - subspace learning
KW - tumor architecture
UR - http://www.scopus.com/inward/record.url?scp=84864859719&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864859719&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2012.6235544
DO - 10.1109/ISBI.2012.6235544
M3 - Conference contribution
AN - SCOPUS:84864859719
SN - 9781457718588
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 302
EP - 305
BT - 2012 9th IEEE International Symposium on Biomedical Imaging
T2 - 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Y2 - 2 May 2012 through 5 May 2012
ER -