  Deep convolutional neural networks (CNNs) have emerged as a new paradigm for
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis (CAD) for
breast cancer directly extract latent features from input mammogram image and
ignore the importance of morphological features. In this paper, we introduce a
novel deep learning framework for mammogram image processing, which computes
mass segmentation and simultaneously predict diagnosis results. Specifically,
our method is constructed in a dual-path architecture that solves the mapping
in a dual-problem manner, with an additional consideration of important shape
and boundary knowledge. One path called the Locality Preserving Learner (LPL),
is devoted to hierarchically extracting and exploiting intrinsic features of
the input. Whereas the other path, called the Conditional Graph Learner (CGL)
focuses on generating geometrical features via modeling pixel-wise image to
mask correlations. By integrating the two learners, both the semantics and
structure are well preserved and the component learning paths in return
complement each other, contributing an improvement to the mass segmentation and
cancer classification problem at the same time. We evaluated our method on two
most used public mammography datasets, DDSM and INbreast. Experimental results
show that DualCoreNet achieves the best mammography segmentation and
classification simultaneously, outperforming recent state-of-the-art models.
