# DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification Source: [DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:29:18.716Z Authors: Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga, UK Arvind Rao, Ganesh Rao Published: 2015-02-03T21:05:23.000Z ## Abstract 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 ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()