  We explore the use of deep learning for breast mass segmentation in
mammograms. By integrating the merits of residual learning and probabilistic
graphical modelling with standard U-Net, we propose a new deep network,
Conditional Residual U-Net (CRU-Net), to improve the U-Net segmentation
performance. Benefiting from the advantage of probabilistic graphical modelling
in the pixel-level labelling, and the structure insights of a deep residual
network in the feature extraction, the CRU-Net provides excellent mass
segmentation performance. Evaluations based on INbreast and DDSM-BCRP datasets
demonstrate that the CRU-Net achieves the best mass segmentation performance
compared to the state-of-art methodologies. Moreover, neither tedious
pre-processing nor post-processing techniques are not required in our
algorithm.
