Learning invariant features of tumor signature

We present a novel method for automated learning of features from unlabeled image patches for classification of tumor architecture. In contrast to manually designed feature detectors (e.g., Gabor basis function), the proposed method utilizes independent subspace analysis to reconstruct a natural representation. Learning is 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. We show that the performance of this method is better than expert designed representation, therefore, promising a wider application of self-learning strategies for tissue characterization.