  The problem of recognizing various types of tissues present in
multi-gigapixel histology images is an important fundamental pre-requisite for
downstream analysis of the tumor microenvironment in a bottom-up analysis
paradigm for computational pathology. In this paper, we propose a deep
dictionary learning approach to solve the problem of tissue phenotyping in
histology images. We propose deep Multi-Resolution Dictionary Learning
(deepMRDL) in order to benefit from deep texture descriptors at multiple
different spatial resolutions. We show the efficacy of the proposed approach
through extensive experiments on four benchmark histology image datasets from
different organs (colorectal cancer, breast cancer and breast lymphnodes) and
tasks (namely, cancer grading, tissue phenotyping, tumor detection and tissue
type classification). We also show that the proposed framework can employ most
off-the-shelf CNNs models to generate effective deep texture descriptors.
