  In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structures. In this paper, we
propose an automatic feature discovery framework via learning class-specific
dictionaries and present a low-complexity method for classification and disease
grading in histopathology. Essentially, our Discriminative Feature-oriented
Dictionary Learning (DFDL) method learns class-specific dictionaries such that
under a sparsity constraint, the learned dictionaries allow representing a new
image sample parsimoniously via the dictionary corresponding to the class
identity of the sample. At the same time, the dictionary is designed to be
poorly capable of representing samples from other classes. Experiments on three
challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian kidney, lung and spleen images
provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University,
and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal
the merits of our proposal over state-of-the-art alternatives. {Moreover, we
demonstrate that DFDL exhibits a more graceful decay in classification accuracy
against the number of training images which is highly desirable in practice
where generous training is often not available
