Abstract
In this paper, a novel coherence-regularized discriminative dictionary learning (CRDDL) algorithm is proposed to deal with histopathological image classification. By incorporating two constructed special regularization terms into an objective function, i.e., the self-coherence within each intra-class dictionary and the mutual coherence between inter-class dictionaries, high-quality discriminative healthy and diseased dictionaries can both be explicitly learned. Furthermore, to balance the reconstruction and discrimination abilities of learned dictionaries, we minimize the reconstruction error of intra-class samples and maximize the reconstruction error of inter-class samples. Finally, reconstruction error vectors are employed to design the classifier of histopathological images. Experimental results demonstrate the improved performance of the proposed CRDDL algorithm in comparison with other previously reported discriminative dictionary learning algorithms.
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Acknowledgements
This article is supported by the National Natural Science Foundation in china (61573299, 61602397) and the Natural Science Foundation of Hunan Province in China (2017JJ3315, 2017JJ2251, 2016JJ3125).
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Tang, H., Li, X., Zhang, X. et al. Coherence-regularized discriminative dictionary learning for histopathological image classification. SIViP 13, 923–931 (2019). https://doi.org/10.1007/s11760-019-01429-0
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DOI: https://doi.org/10.1007/s11760-019-01429-0