Abstract
Breast histology images classification is a time- and labor-intensive task due to the complicated structural and textural information contained. Recent deep learning-based methods are less accurate due to the ignorance of the interfering multiscale contextual information in histology images. In this paper, we propose the multiscale spatial attention network (MSA-Net) to deal with these challenges. We first perform adaptive spatial transformation on histology microscopy images at multiple scales using a spatial attention (SA) module to make the model focus on discriminative content. Then we employ a classification network to categorize the transformed images and use the ensemble of the predictions obtained at multiple scales as the classification result. We evaluated our MSA-Net against four state-of-the-art methods on the BACH challenge dataset. Our results show that the proposed MSA-Net achieves a higher accuracy than the rest methods in the five-fold cross validation on training data, and reaches the 2nd place in the online verification.
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Acknowledgement
This work was supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grant JCYJ20180306171334997, in part by the National Natural Science Foundation of China under Grant 61771397 and 61902322, in part by the Fundamental Research Funds for the Central Universities under Grant 3102019G2019KY0001, in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University under Grants ZZ2019029, and in part by the Project for Graduate Innovation team of Northwestern Polytechnical University.
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Yang, Z., Ran, L., Xia, Y., Zhang, Y. (2020). MSA-Net: Multiscale Spatial Attention Network for the Classification of Breast Histology Images. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_26
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