Title
Discriminative Dictionary Learning Algorithm With Pairwise Local Constraints For Histopathological Image Classification
Abstract
Histopathological image contains rich pathological information that is valued for the aided diagnosis of many diseases such as cancer. An important issue in histopathological image classification is how to learn a high-quality discriminative dictionary due to diverse tissue pattern, a variety of texture, and different morphologies structure. In this paper, we propose a discriminative dictionary learning algorithm with pairwise local constraints (PLCDDL) for histopathological image classification. Inspired by the one-to-one mapping between dictionary atom and profile, we learn a pair of discriminative graph Laplacian matrices that are less sensitive to noise or outliers to capture the locality and discriminating information of data manifold by utilizing the local geometry information of category-specific dictionaries rather than input data. Furthermore, graph-based pairwise local constraints are designed and incorporated into the original dictionary learning model to effectively encode the locality consistency with intra-class samples and the locality inconsistency with inter-class samples. Specifically, we learn the discriminative localities for representations by jointly optimizing both the intra-class locality and inter-class locality, which can significantly improve the discriminability and robustness of dictionary. Extensive experiments on the challenging datasets verify that the proposed PLCDDL algorithm can achieve a better classification accuracy and powerful robustness compared with the state-of-the-art dictionary learning methods.
Year
DOI
Venue
2021
10.1007/s11517-020-02281-y
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Keywords
DocType
Volume
Discriminative dictionary learning, Local geometry information, Graph-based pairwise local constraints, Histopathological image classification
Journal
59
Issue
ISSN
Citations 
1
0140-0118
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Hongzhong Tang132.83
Lizhen Mao200.34
Shuying Zeng300.34
Shijun Deng400.34
Zhaoyang Ai500.34