Journal
PATTERN RECOGNITION
Volume 89, Issue -, Pages 67-76Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.12.013
Keywords
Principal component analysis network (PCANet); Quaternion algebra; Quaternion Grassmann averages network (QGANet); Color histopathological image
Funding
- National Natural Science Foundation of China [61471231, 81627804, 61671281, 11471208]
- Shanghai Science and Technology Foundation [17411953400, 18010500600]
- Shanghai Hospital Development Center [16CR3061B]
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Histopathological image analysis works as 'gold standard' for cancer diagnosis. Its computer-aided approach has attracted considerable attention in the field of digital pathology, which highly depends on the feature representation for histopathological images. The principal component analysis network (PCANet) is a novel unsupervised deep learning framework that has shown its effectiveness for feature representation learning. However, PCA is susceptible to noise and outliers to affect the performance of PCANet. The Grassmann average (GA) is superior to PCA on robustness. In this work, a GA network (GANet) algorithm is proposed by embedding GA algorithm into the PCANet framework. Moreover, since quaternion algebra is an excellent tool to represent color images, a quaternion-based GANet (QGANet) algorithm is further developed to learn effective feature representations containing color information for histopathological images. The experimental results based on three histopathological image datasets indicate that the proposed QGANet achieves the best performance on the classification of color histopathological images among all the compared algorithms. (C) 2018 Elsevier Ltd. All rights reserved.
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