Simultaneous feature extraction and dictionary learning using deep learning architectures for characterization of images of heterogeneous tissue samples

Abstract

Apparatus, methods, and computer-readable media are provided for simultaneous feature extraction and dictionary learning from heterogeneous tissue images, without the need of prior local labeling. A convolutional autoencoder is adapted and enhanced to jointly learn a feature extraction algorithm and a dictionary of representative atoms. While training the autoencoder an image patch is tiled in sub-patches and only the highest activation value per sub-patch is kept. Thus, only a subset of spatially constrained values per patch is used for reconstruction. The deconvolutional filters are the dictionary elements, and only a deconvolution layer is used for these elements. Embodiments described herein may be provided for use in models for representing local tissue heterogeneity for better disease progression understanding and thus treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.