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Open AccessArticle

Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification

School of Information Science and Engineering, Yunnan University, Kunming 650091, China
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Cancers 2019, 11(12), 1901; https://doi.org/10.3390/cancers11121901
Received: 5 October 2019 / Revised: 10 November 2019 / Accepted: 26 November 2019 / Published: 29 November 2019
(This article belongs to the Collection Application of Bioinformatics in Cancers)
In this paper, we present a new deep learning model to classify hematoxylin–eosin-stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas). Our model uses a parallel structure consist of a convolutional neural network (CNN) and a recurrent neural network (RNN) for image feature extraction, which is greatly different from the common existed serial method of extracting image features by CNN and then inputting them into RNN. Then, we introduce a special perceptron attention mechanism, which is derived from the natural language processing (NLP) field, to unify the features extracted by the two different neural network structures of the model. In the convolution layer, general batch normalization is replaced by the new switchable normalization method. And the latest regularization technology, targeted dropout, is used to substitute for the general dropout in the last three fully connected layers of the model. In the testing phase, we use the model fusion method and test time augmentation technology on three different datasets of hematoxylin–eosin-stained breast biopsy images. The results demonstrate that our model significantly outperforms state-of-the-art methods. View Full-Text
Keywords: breast cancer; biopsy image; DenseNet; LSTM; attention; switchable normalization; targeted dropout; test time augmentation breast cancer; biopsy image; DenseNet; LSTM; attention; switchable normalization; targeted dropout; test time augmentation
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Yao, H.; Zhang, X.; Zhou, X.; Liu, S. Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. Cancers 2019, 11, 1901.

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