  Mass segmentation is an important task in mammogram analysis, providing
effective morphological features and regions of interest (ROI) for mass
detection and classification. Inspired by the success of using deep
convolutional features for natural image analysis and conditional random fields
(CRF) for structural learning, we propose an end-to-end network for
mammographic mass segmentation. The network employs a fully convolutional
network (FCN) to model potential function, followed by a CRF to perform
structural learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with position priori for the task. Due to the
small size of mammogram datasets, we use adversarial training to control
over-fitting. Four models with different convolutional kernels are further
fused to improve the segmentation results. Experimental results on two public
datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined
with adversarial training achieves the-state-of-the-art results.
