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Proceedings Paper

Correlation-filter enhanced meta-learning for classification of biomedical images
Author(s): Quan Wen; Shiying Wang; Danmin Li; Feifei Chen
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Paper Abstract

Recent deep learning methods have demonstrated remarkable impact on the classification of biomedical images. In this paper, we proposed a correlation-filter enhanced meta-learning approach for the classification of biomedical images. Firstly, in the training stage, we use the training samples to optimize the model parameters of meta-learning. Secondly, in the testing stage, we utilize the data samples of the new task to generalize the model parameters. Thirdly, the nearest neighbor image from one sample batch is searched for the new instance image, with the classifying score provided by the meta-learning model. Fourthly, the template of the circular cross-correlation filter is optimized in the Fourier domain, using the new instance image and its nearest neighbor image. Fifthly, the support weight of the sample batch is calculated for the classified label by the meta-learning model. Finally, we propose the multi-batch voting mechanism to decide the label of the new instance based on the correlation-filter template. Experiments on the classification of biomedical images demonstrated the effectiveness of our approach, compared with other state-of-the-art methods.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691L (6 May 2019); doi: 10.1117/12.2524271
Show Author Affiliations
Quan Wen, Univ. of Electronic Science and Technology of China (China)
Shiying Wang, Univ. of Electronic Science and Technology of China (China)
Danmin Li, Univ. of Electronic Science and Technology of China (China)
Feifei Chen, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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