  The high cost of generating expert annotations, poses a strong limitation for
supervised machine learning methods in medical imaging. Weakly supervised
methods may provide a solution to this tangle. In this study, we propose a
novel deep learning architecture for multi-class classification of mammograms
according to the severity of their containing anomalies, having only a global
tag over the image. The suggested scheme further allows localization of the
different types of findings in full resolution. The new scheme contains a dual
branch network that combines region-level classification with region ranking.
We evaluate our method on a large multi-center mammography dataset including
$\sim$3,000 mammograms with various anomalies and demonstrate the advantages of
the proposed method over a previous weakly-supervised strategy.
