144 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
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering
Scene parsing has attracted a lot of attention in computer vision. While
parametric models have proven effective for this task, they cannot easily
incorporate new training data. By contrast, nonparametric approaches, which
bypass any learning phase and directly transfer the labels from the training
data to the query images, can readily exploit new labeled samples as they
become available. Unfortunately, because of the computational cost of their
label transfer procedures, state-of-the-art nonparametric methods typically
filter out most training images to only keep a few relevant ones to label the
query. As such, these methods throw away many images that still contain
valuable information and generally obtain an unbalanced set of labeled samples.
In this paper, we introduce a nonparametric approach to scene parsing that
follows a sample-and-filter strategy. More specifically, we propose to sample
labeled superpixels according to an image similarity score, which allows us to
obtain a balanced set of samples. We then formulate label transfer as an
efficient filtering procedure, which lets us exploit more labeled samples than
existing techniques. Our experiments evidence the benefits of our approach over
state-of-the-art nonparametric methods on two benchmark datasets.Comment: Please refer to the CVPR-2016 version of this manuscrip
- …