  This paper presents results of applying Inception v4 deep convolutional
neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part
a. The Challenge task is to classify breast cancer biopsy results, presented in
form of hematoxylin and eosin stained images. Breast cancer classification is
of primary interest to the medical practitioners and thus binary classification
of breast cancer images have been under investigation by many researchers, but
multi-class categorization of histology breast images have been challenging due
to the subtle differences among the categories. In this work extensive data
augmentation is conducted to reduce overfitting and effectiveness of committee
of several Inception v4 networks is studied. We report 89% accuracy on 4 class
classification task and 93.7% on carcinoma/non-carcinoma two class
classification task using our test set of 80 images.
