DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

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

Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 2015

References in corpus (1)

• Quality-based Multimodal Classification Using Tree-Structured Sparsity

Cited by in corpus (9)

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• ICR: Iterative Convex Refinement for Sparse Signal Recovery Using Spike and Slab Priors
• Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
• Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification
• Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning
• Enhanced Signal Recovery via Sparsity Inducing Image Priors
• Analysis-synthesis model learning with shared features: a new framework for histopathological image classification
• Localized Dictionary design for Geometrically Robust Sonar ATR
• Adaptive matching pursuit for sparse signal recovery