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15 February 2021 Human epithelial-2 cell image classification using deep unsupervised learning and gradient boosting trees
Xu Wang, Tanqiu Jiang, Hengxing Cai II
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Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 1160105 (2021) https://doi.org/10.1117/12.2582023
Event: SPIE Medical Imaging, 2021, Online Only
Abstract
In this paper, an ensemble learning framework is proposed for HEp-2 cell images, aiming to making use of both handcrafted features and deep learning-based methods. Firstly, deep unsupervised learning is employed to extract features. Then, a gradient boosting trees-based classifier is trained using both handcrafted features and deep learning-based features. Extensive experiments are conducted on benchmark datasets to test the efficiency and robustness of the proposed framework. Experiment results demonstrate hat the proposed framework yield excellent performances compared with existing deep learning-based models.
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Xu Wang, Tanqiu Jiang, and Hengxing Cai II "Human epithelial-2 cell image classification using deep unsupervised learning and gradient boosting trees", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 1160105 (15 February 2021); https://doi.org/10.1117/12.2582023
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KEYWORDS
Image classification
Machine learning
Convolutional neural networks
Image analysis
Performance modeling
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