A meta-learning method for histopathology image classification based on LSTM-model

Quan Wen; Jiazi Yan; Boling Liu; Daying Meng; Siyi Li

doi:10.1117/12.2524387

6 May 2019 A meta-learning method for histopathology image classification based on LSTM-model

Quan Wen, Jiazi Yan, Boling Liu, Daying Meng, Siyi Li

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110691H (2019) https://doi.org/10.1117/12.2524387
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

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.

Citation Download Citation

Quan Wen, Jiazi Yan, Boling Liu, Daying Meng, and Siyi Li "A meta-learning method for histopathology image classification based on LSTM-model", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691H (6 May 2019); https://doi.org/10.1117/12.2524387

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