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A meta-learning method for histopathology image classification based on LSTM-model
Author(s): Quan Wen; Jiazi Yan; Boling Liu; Daying Meng; Siyi Li
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Paper Abstract

The rapid development of meta-learning methods enables the generalized classification of histopathology images with only a handful of new training images. Meta-learning is also named as learning to learn. In this study, we propose a LSTM-model based meta-learning framework for the histopathology image classification. We apply the DoubleOpponent (DO) neurons to model the texture patterns of histopathology images. And the LSTM-model is utilized for the optimization of the meta-learning algorithm to classify the histopathology images. Experiment results on real dataset demonstrated that the proposed method leads in all the measures, namely, recall, precision, F-measure and accuracy.

Paper Details

Date Published: 6 May 2019
PDF: 5 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691H (6 May 2019); doi: 10.1117/12.2524387
Show Author Affiliations
Quan Wen, Univ. of Electronic Science and Technology of China (China)
Jiazi Yan, Univ. of Electronic Science and Technology of China (China)
Boling Liu, Univ. of Electronic Science and Technology of China (China)
Daying Meng, Univ. of Electronic Science and Technology of China (China)
Siyi Li, 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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