  Accurate diagnosis of breast cancer in histopathology images is challenging
due to the heterogeneity of cancer cell growth as well as of a variety of
benign breast tissue proliferative lesions. In this paper, we propose a
practical and self-interpretable invasive cancer diagnosis solution. With
minimum annotation information, the proposed method mines contrast patterns
between normal and malignant images in unsupervised manner and generates a
probability map of abnormalities to verify its reasoning. Particularly, a fully
convolutional autoencoder is used to learn the dominant structural patterns
among normal image patches. Patches that do not share the characteristics of
this normal population are detected and analyzed by one-class support vector
machine and 1-layer neural network. We apply the proposed method to a public
breast cancer image set. Our results, in consultation with a senior
pathologist, demonstrate that the proposed method outperforms existing methods.
The obtained probability map could benefit the pathology practice by providing
visualized verification data and potentially leads to a better understanding of
data-driven diagnosis solutions.
