Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification.
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 tex...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 1; pp. 153 - 165 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148139430&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148139430 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2021 vid: 59 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148139430 147883401 148139430 NLM33386592 10.1007/s11517-020-02281-y NLM33386592 148139430 ppf: 153 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification. aug: au: Tang, Hongzhong Mao, Lizhen Zeng, Shuying Deng, Shijun Ai, Zhaoyang affil: Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, People's Republic of China sug: subj: Neoplasms Algorithms Ferrans and Powers Quality of Life Index ab: 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. Graphical abstract 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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