  Breast cancer is the most frequently diagnosed cancer and leading cause of
cancer-related death among females worldwide. In this article, we investigate
the applicability of densely connected convolutional neural networks to the
problems of histology image classification and whole slide image segmentation
in the area of computer-aided diagnoses for breast cancer. To this end, we
study various approaches for transfer learning and apply them to the data set
from the 2018 grand challenge on breast cancer histology images (BACH).
