A meta-learning method for histopathology image classification based on LSTM-model
Quan Wen, Jiazi Yan, Boling Liu, Daying Meng, Siyi Li · 2019
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.