DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
This work addresses the problem of automated disease grading in histopathology for medical diagnosis, presenting an incremental improvement with a novel method for a known bottleneck.
The paper tackled the challenge of feature extraction for histopathological image classification by proposing DFDL, a discriminative feature-oriented dictionary learning method that learns class-specific features, achieving improved performance over state-of-the-art methods on three real-world image databases.
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 structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features 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 features which are suitable for representing samples from the same class while are 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 lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methods