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

TL;DR

This paper introduces DFDL, a novel dictionary learning framework that automatically discovers discriminative, class-specific features for histopathological image classification, improving accuracy across diverse datasets.

Contribution

The paper presents a low-complexity, automatic feature discovery method that learns class-specific features for better histopathological image classification.

Findings

DFDL outperforms state-of-the-art methods on three real-world datasets.

The method effectively captures class-specific features for diverse histology images.

DFDL demonstrates robustness across different types of histopathological images.

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