  Automated histopathological image analysis offers exciting opportunities for
the early diagnosis of several medical conditions including cancer. There are
however stiff practical challenges: 1.) discriminative features from such
images for separating diseased vs. healthy classes are not readily apparent,
and 2.) distinct classes, e.g. healthy vs. stages of disease continue to share
several geometric features. We propose a novel Analysis-synthesis model
Learning with Shared Features algorithm (ALSF) for classifying such images more
effectively. In ALSF, a joint analysis and synthesis learning model is
introduced to learn the classifier and the feature extractor at the same time.
In this way, the computation load in patch-level based image classification can
be much reduced. Crucially, we integrate into this framework the learning of a
low rank shared dictionary and a shared analysis operator, which more
accurately represents both similarities and differences in histopathological
images from distinct classes. ALSF is evaluated on two challenging databases:
(1) kidney tissue images provided by the Animal Diagnosis Lab (ADL) at the
Pennsylvania State University and (2) brain tumor images from The Cancer Genome
Atlas (TCGA) database. Experimental results confirm that ALSF can offer
benefits over state of the art alternatives.
