26 research outputs found
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with
arXiv:1612.0597