  Breast Cancer is a major cause of death worldwide among women. Hematoxylin
and Eosin (H&E) stained breast tissue samples from biopsies are observed under
microscopes for the primary diagnosis of breast cancer. In this paper, we
propose a deep learning-based method for classification of H&E stained breast
tissue images released for BACH challenge 2018 by fine-tuning Inception-v3
convolutional neural network (CNN) proposed by Szegedy et al. These images are
to be classified into four classes namely, i) normal tissue, ii) benign tumor,
iii) in-situ carcinoma and iv) invasive carcinoma. Our strategy is to extract
patches based on nuclei density instead of random or grid sampling, along with
rejection of patches that are not rich in nuclei (non-epithelial) regions for
training and testing. Every patch (nuclei-dense region) in an image is
classified in one of the four above mentioned categories. The class of the
entire image is determined using majority voting over the nuclear classes. We
obtained an average four class accuracy of 85% and an average two class
(non-cancer vs. carcinoma) accuracy of 93%, which improves upon a previous
benchmark by Araujo et al.
