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Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification

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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.

The proposed PLCDDL algorithm. 1) A pair of graph Laplacian matrices are first learned based on the class-specific dictionaries. 2) Graph-based pairwise local constraints are designed to transfer the locality for coding coefficients. 3) Class-specific dictionaries can be further updated.

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Acknowledgments

We would especially like to thank Associate Professor Chaoyang Ai for his contribution to the English revision of this manuscript.

Funding

This article is supported by Science and Technology Plan Project of Hunan Province in China (2016TP1020), Open fund project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang normal university, Natural Science Foundation of Hunan Province in China (2020JJ4588, 2020JJ4090).

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, People’s Republic of China

    Hongzhong Tang, Lizhen Mao, Shuying Zeng & Shijun Deng

  2. College of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan, People’s Republic of China

    Hongzhong Tang & Shijun Deng

  3. Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan, People’s Republic of China

    Hongzhong Tang

  4. Institute of Biophysics Linguistics, College of Foreign Languages, Hunan University, Changsha, Hunan, People’s Republic of China

    Zhaoyang Ai

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  1. Hongzhong Tang
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  2. Lizhen Mao
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  3. Shuying Zeng
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Correspondence to Hongzhong Tang.

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Tang, H., Mao, L., Zeng, S. et al. Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification. Med Biol Eng Comput 59, 153–164 (2021). https://doi.org/10.1007/s11517-020-02281-y

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  • DOI: https://doi.org/10.1007/s11517-020-02281-y

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