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This paper presents results of applying Inception v4 deep convolutionalneural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, parta. The Challenge task is to classify breast cancer biopsy results, presented inform of hematoxylin and eosin stained images. Breast cancer classification isof primary interest to the medical practitioners and thus binary classificationof breast cancer images have been under investigation by many researchers, butmulti-class categorization of histology breast images have been challenging dueto the subtle differences among the categories. In this work extensive dataaugmentation is conducted to reduce overfitting and effectiveness of committeeof several Inception v4 networks is studied. We report 89% accuracy on 4 classclassification task and 93.7% on carcinoma/non-carcinoma two classclassification task using our test set of 80 images.

Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images