One of the most important prognostic markers to assess proliferation activity of breast tumors is estimating the number of mitotic figures in H&E stained tissue. We propose the use of a recently published convolutional neural network architecture, Wide Residual Networks, for mitosis detection in breast histology images. The model is trained to classify each pixel of on an image using as context a patch centered on the pixel. We apply post-processing on the network output in order to filter out noise and select true mitosis. Finally, we combine the output of several networks using majority vote. Our approach ranked 2nd in the MICCAI TUPAC 2016 competition for mitosis detection, outperforming most other contestants by a significant margin.