  In the last years, neural networks have proven to be a powerful framework for
various image analysis problems. However, some application domains have
specific limitations. Notably, digital pathology is an example of such fields
due to tremendous image sizes and quite limited number of training examples
available. In this paper, we adopt state-of-the-art convolutional neural
networks (CNN) architectures for digital pathology images analysis. We propose
to classify image patches to increase effective sample size and then to apply
an ensembling technique to build prediction for the original images. To
validate the developed approaches, we conducted experiments with \textit{Breast
Cancer Histology Challenge} dataset and obtained 90\% accuracy for the 4-class
tissue classification task.
