  We present, for the first time, a novel deep neural network architecture
called \dcn with a dual-path connection between the input image and output
class label for mammogram image processing. This architecture is built upon
U-Net, which non-linearly maps the input data into a deep latent space. One
path of the \dcnn, the locality preserving learner, is devoted to
hierarchically extracting and exploiting intrinsic features of the input, while
the other path, called the conditional graph learner, focuses on modeling the
input-mask correlations. The learned mask is further used to improve
classification results, and the two learning paths complement each other. By
integrating the two learners our new architecture provides a simple but
effective way to jointly learn the segmentation and predict the class label.
Benefiting from the powerful expressive capacity of deep neural networks a more
discriminative representation can be learned, in which both the semantics and
structure are well preserved. Experimental results show that \dcn achieves the
best mammography segmentation and classification simultaneously, outperforming
recent state-of-the-art models.
